text:
@inproceedings{10.1145/2488388.2488390,
author = {Abbar, Sofiane and Amer-Yahia, Sihem and Indyk, Piotr and Mahabadi, Sepideh},
title = {Real-Time Recommendation of Diverse Related Articles},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488390},
doi = {10.1145/2488388.2488390},
abstract = {News articles typically drive a lot of traffic in the form of comments posted by users on a news site. Such user-generated content tends to carry additional information such as entities and sentiment. In general, when articles are recommended to users, only popularity (e.g., most shared and most commented), recency, and sometimes (manual) editors' picks (based on daily hot topics), are considered. We formalize a novel recommendation problem where the goal is to find the closest most diverse articles to the one the user is currently browsing. Our diversity measure incorporates entities and sentiment extracted from comments. Given the real-time nature of our recommendations, we explore the applicability of nearest neighbor algorithms to solve the problem. Our user study on real opinion articles from aljazeera.net and reuters.com validates the use of entities and sentiment extracted from articles and their comments to achieve news diversity when compared to content-based diversity. Finally, our performance experiments show the real-time feasibility of our solution.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {1–12},
numpages = {12},
keywords = {diversity, recommender system, social media},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488391,
author = {Agrawal, Rahul and Gupta, Archit and Prabhu, Yashoteja and Varma, Manik},
title = {Multi-Label Learning with Millions of Labels: Recommending Advertiser Bid Phrases for Web Pages},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488391},
doi = {10.1145/2488388.2488391},
abstract = {Recommending phrases from web pages for advertisers to bid on against search engine queries is an important research problem with direct commercial impact. Most approaches have found it infeasible to determine the relevance of all possible queries to a given ad landing page and have focussed on making recommendations from a small set of phrases extracted (and expanded) from the page using NLP and ranking based techniques. In this paper, we eschew this paradigm, and demonstrate that it is possible to efficiently predict the relevant subset of queries from a large set of monetizable ones by posing the problem as a multi-label learning task with each query being represented by a separate label.We develop Multi-label Random Forests to tackle problems with millions of labels. Our proposed classifier has prediction costs that are logarithmic in the number of labels and can make predictions in a few milliseconds using 10 Gb of RAM. We demonstrate that it is possible to generate training data for our classifier automatically from click logs without any human annotation or intervention. We train our classifier on tens of millions of labels, features and training points in less than two days on a thousand node cluster. We develop a sparse semi-supervised multi-label learning formulation to deal with training set biases and noisy labels harvested automatically from the click logs. This formulation is used to infer a belief in the state of each label for each training ad and the random forest classifier is extended to train on these beliefs rather than the given labels. Experiments reveal significant gains over ranking and NLP based techniques on a large test set of 5 million ads using multiple metrics.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {13–24},
numpages = {12},
keywords = {multi-label learning, random forests, large scale learning, bid phrase recommendation, semi-supervised learning},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488392,
author = {Ahmed, Amr and Hong, Liangjie and Smola, Alexander J.},
title = {Hierarchical Geographical Modeling of User Locations from Social Media Posts},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488392},
doi = {10.1145/2488388.2488392},
abstract = {With the availability of cheap location sensors, geotagging of messages in online social networks is proliferating. For instance, Twitter, Facebook, Foursquare, and Google+ provide these services both explicitly by letting users choose their location or implicitly via a sensor. This paper presents an integrated generative model of location and message content. That is, we provide a model for combining distributions over locations, topics, and over user characteristics, both in terms of location and in terms of their content preferences. Unlike previous work which modeled data in a flat pre-defined representation, our model automatically infers both the hierarchical structure over content and over the size and position of geographical locations. This affords significantly higher accuracy --- location uncertainty is reduced by 40% relative to the best previous results [21] achieved on location estimation from Tweets.We achieve this goal by proposing a new statistical model, the nested Chinese Restaurant Franchise (nCRF), a hierarchical model of tree distributions. Much statistical structure is shared between users. That said, each user has his own distribution over interests and places. The use of the nCRF allows us to capture the following effects: (1) We provide a topic model for Tweets; (2) We obtain location specific topics; (3) We infer a latent distribution of locations; (4) We provide a joint hierarchical model of topics and locations; (5) We infer personalized preferences over topics and locations within the above model. In doing so, we are both able to obtain accurate estimates of the location of a user based on his tweets and to obtain a detailed estimate of a geographical language model.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {25–36},
numpages = {12},
keywords = {twitter, user profiling, non-paremetric bayesian models, chinese restaurant process, topic models, geolocation},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488393,
author = {Ahmed, Amr and Shervashidze, Nino and Narayanamurthy, Shravan and Josifovski, Vanja and Smola, Alexander J.},
title = {Distributed Large-Scale Natural Graph Factorization},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488393},
doi = {10.1145/2488388.2488393},
abstract = {Natural graphs, such as social networks, email graphs, or instant messaging patterns, have become pervasive through the internet. These graphs are massive, often containing hundreds of millions of nodes and billions of edges. While some theoretical models have been proposed to study such graphs, their analysis is still difficult due to the scale and nature of the data.We propose a framework for large-scale graph decomposition and inference. To resolve the scale, our framework is distributed so that the data are partitioned over a shared-nothing set of machines. We propose a novel factorization technique that relies on partitioning a graph so as to minimize the number of neighboring vertices rather than edges across partitions. Our decomposition is based on a streaming algorithm. It is network-aware as it adapts to the network topology of the underlying computational hardware. We use local copies of the variables and an efficient asynchronous communication protocol to synchronize the replicated values in order to perform most of the computation without having to incur the cost of network communication. On a graph of 200 million vertices and 10 billion edges, derived from an email communication network, our algorithm retains convergence properties while allowing for almost linear scalability in the number of computers.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {37–48},
numpages = {12},
keywords = {matrix factorization, graph algorithms, large-scale machine learning, graph factorization, distributed optimization, asynchronous algorithms},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488394,
author = {Ajmera, Jitendra and Ahn, Hyung-iL and Nagarajan, Meena and Verma, Ashish and Contractor, Danish and Dill, Stephen and Denesuk, Matthew},
title = {A CRM System for Social Media: Challenges and Experiences},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488394},
doi = {10.1145/2488388.2488394},
abstract = {The social Customer Relationship Management (CRM) landscape is attracting significant attention from customers and enterprises alike as a sustainable channel for tracking, managing and improving customer relations. Enterprises are taking a hard look at this open, unmediated platform because the community effect generated on this channel can have a telling effect on their brand image, potential market opportunity and customer loyalty. In this work we present our experiences in building a system that mines conversations on social platforms to identify and prioritize those posts and messages that are relevant to enterprises. The system presented in this work aims to empower an agent or a representative in an enterprise to monitor, track and respond to customer communication while also encouraging community participation.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {49–58},
numpages = {10},
keywords = {social crm, social media},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488395,
author = {Akhawe, Devdatta and Amann, Bernhard and Vallentin, Matthias and Sommer, Robin},
title = {Here's My Cert, so Trust Me, Maybe? Understanding TLS Errors on the Web},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488395},
doi = {10.1145/2488388.2488395},
abstract = {When browsers report TLS errors, they cannot distinguish between attacks and harmless server misconfigurations; hence they leave it to the user to decide whether continuing is safe. However, actual attacks remain rare. As a result, users quickly become used to "false positives" that deplete their attention span, making it unlikely that they will pay sufficient scrutiny when a real attack comes along. Consequently, browser vendors should aim to minimize the number of low-risk warnings they report. To guide that process, we perform a large-scale measurement study of common TLS warnings. Using a set of passive network monitors located at different sites, we identify the prevalence of warnings for a total population of about 300,000 users over a nine-month period. We identify low-risk scenarios that consume a large chunk of the user attention budget and make concrete recommendations to browser vendors that will help maintain user attention in high-risk situations. We study the impact on end users with a data set much larger in scale than the data sets used in previous TLS measurement studies. A key novelty of our approach involves the use of internal browser code instead of generic TLS libraries for analysis, providing more accurate and representative results.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {59–70},
numpages = {12},
keywords = {warnings, tls},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488396,
author = {Aly, Mohamed and Pandey, Sandeep and Josifovski, Vanja and Punera, Kunal},
title = {Towards a Robust Modeling of Temporal Interest Change Patterns for Behavioral Targeting},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488396},
doi = {10.1145/2488388.2488396},
abstract = {Modern web-scale behavioral targeting platforms leverage historical activity of billions of users to predict user interests and inclinations, and consequently future activities. Future activities of particular interest involve purchases or transactions, and are referred to as conversions. Unlike ad-clicks, conversions directly translate to advertiser's revenue, and thus provide a very concrete metric for return on advertising investment. A typical behavioral targeting system faces two main challenges: the web-scale amounts of user histories to process on a daily basis, and the relative sparsity of conversions (compared to clicks in a traditional setting). These challenges call for generation of effective and efficient user profiles. Most existing works use the historical intensity of a user's interest in various topics to model future interest. In this paper we explore how the change in user behavior can be used to predict future actions and show how it complements the traditional models of decaying interest and action recency to build a complete picture about the user interests and better predict conversions. Our evaluation over a real-world set of campaigns indicates that the combination of change of interest, decaying intensity, and action recency helps in: 1) scoring significant improvements in optimizing for conversions over traditional baselines, 2) substantially improving the targeting efficiency for campaigns with highly sparse conversions, and 3) highly reducing the overall history sizes used in targeting. Furthermore, our techniques have been deployed to production and scored a substantial improvement in targeting performance while imposing a negligible overhead in terms of overall platform running time.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {71–82},
numpages = {12},
keywords = {display advertising, behavioral targeting, user modeling, time-based features},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488397,
author = {Alzoubi, Hussein A. and Rabinovich, Michael and Spatscheck, Oliver},
title = {The Anatomy of LDNS Clusters: Findings and Implications for Web Content Delivery},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488397},
doi = {10.1145/2488388.2488397},
abstract = {We present a large-scale measurement of clusters of hosts sharing the same local DNS servers. We analyze properties of these "LDNS clusters" from the perspective of content delivery networks, which commonly use DNS for load distribution. We found that not only LDNS clusters differ widely in terms of their size and geographical compactness but that the largest clusters are actually extremely compact. This suggests potential benefits of a load distribution strategy with nuanced treatment of different LDNS clusters based on the combination of their size and compactness. We further observed interesting variations in LDNS setups including a wide use of "LDNS pools" (which as we explain in the paper are different from setups where end-hosts simply utilize multiple resolvers).},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {83–94},
numpages = {12},
keywords = {dns, content delivery network, request routing},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488398,
author = {Anderson, Ashton and Huttenlocher, Daniel and Kleinberg, Jon and Leskovec, Jure},
title = {Steering User Behavior with Badges},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488398},
doi = {10.1145/2488388.2488398},
abstract = {An increasingly common feature of online communities and social media sites is a mechanism for rewarding user achievements based on a system of badges. Badges are given to users for particular contributions to a site, such as performing a certain number of actions of a given type. They have been employed in many domains, including news sites like the Huffington Post, educational sites like Khan Academy, and knowledge-creation sites like Wikipedia and Stack Overflow. At the most basic level, badges serve as a summary of a user's key accomplishments; however, experience with these sites also shows that users will put in non-trivial amounts of work to achieve particular badges, and as such, badges can act as powerful incentives. Thus far, however, the incentive structures created by badges have not been well understood, making it difficult to deploy badges with an eye toward the incentives they are likely to create.In this paper, we study how badges can influence and steer user behavior on a site---leading both to increased participation and to changes in the mix of activities a user pursues on the site. We introduce a formal model for reasoning about user behavior in the presence of badges, and in particular for analyzing the ways in which badges can steer users to change their behavior. To evaluate the main predictions of our model, we study the use of badges and their effects on the widely used Stack Overflow question-answering site, and find evidence that their badges steer behavior in ways closely consistent with the predictions of our model. Finally, we investigate the problem of how to optimally place badges in order to induce particular user behaviors. Several robust design principles emerge from our framework that could potentially aid in the design of incentives for a broad range of sites.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {95–106},
numpages = {12},
keywords = {badges, badge placement problem, steering},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488399,
author = {Benson, Edward O. and Karger, David R.},
title = {Cascading Tree Sheets and Recombinant HTML: Better Encapsulation and Retargeting of Web Content},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488399},
doi = {10.1145/2488388.2488399},
abstract = {Cascading Style Sheets (CSS) took a valuable step towards separating web content from presentation. But HTML pages still contain large amounts of "design scaffolding" needed to hierarchically layer content for proper presentation. This paper presents Cascading Tree Sheets (CTS), a CSS-like language for separating this presentational HTML from real content. With CTS, authors can use standard CSS selectors to describe how to graft presentational scaffolding onto their pure-content HTML. This improved separation of content from presentation enables even naive authors to incorporate rich layouts (including interactive Javascript) into their own pages simply by linking to a tree sheet and adding some class names to their HTML.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {107–118},
numpages = {12},
keywords = {cts, web authoring, css, tree sheets, html, xslt},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488400,
author = {Beutel, Alex and Xu, Wanhong and Guruswami, Venkatesan and Palow, Christopher and Faloutsos, Christos},
title = {CopyCatch: Stopping Group Attacks by Spotting Lockstep Behavior in Social Networks},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488400},
doi = {10.1145/2488388.2488400},
abstract = {How can web services that depend on user generated content discern fraudulent input by spammers from legitimate input? In this paper we focus on the social network Facebook and the problem of discerning ill-gotten Page Likes, made by spammers hoping to turn a profit, from legitimate Page Likes. Our method, which we refer to as CopyCatch, detects lockstep Page Like patterns on Facebook by analyzing only the social graph between users and Pages and the times at which the edges in the graph (the Likes) were created. We offer the following contributions: (1) We give a novel problem formulation, with a simple concrete definition of suspicious behavior in terms of graph structure and edge constraints. (2) We offer two algorithms to find such suspicious lockstep behavior - one provably-convergent iterative algorithm and one approximate, scalable MapReduce implementation. (3) We show that our method severely limits "greedy attacks" and analyze the bounds from the application of the Zarankiewicz problem to our setting. Finally, we demonstrate and discuss the effectiveness of CopyCatch at Facebook and on synthetic data, as well as potential extensions to anomaly detection problems in other domains. CopyCatch is actively in use at Facebook, searching for attacks on Facebook's social graph of over a billion users, many millions of Pages, and billions of Page Likes.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {119–130},
numpages = {12},
keywords = {anomaly detection, social networks, bipartite cores, mapreduce},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@dataset{10.1145/review-2488388.2488400_R49841,
author = {Rodrigues, Jose F},
title = {Review ID:R49841 for DOI: 10.1145/2488388.2488400},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/review-2488388.2488400_R49841}
}

@inproceedings{10.1145/2488388.2488401,
author = {Bi, Bin and Shokouhi, Milad and Kosinski, Michal and Graepel, Thore},
title = {Inferring the Demographics of Search Users: Social Data Meets Search Queries},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488401},
doi = {10.1145/2488388.2488401},
abstract = {Knowing users' views and demographic traits offers a great potential for personalizing web search results or related services such as query suggestion and query completion. Such signals however are often only available for a small fraction of search users, namely those who log in with their social network account and allow its use for personalization of search results. In this paper, we offer a solution to this problem by showing how user demographic traits such as age and gender, and even political and religious views can be efficiently and accurately inferred based on their search query histories. This is accomplished in two steps; we first train predictive models based on the publically available myPersonality dataset containing users' Facebook Likes and their demographic information. We then match Facebook Likes with search queries using Open Directory Project categories. Finally, we apply the model trained on Facebook Likes to large-scale query logs of a commercial search engine while explicitly taking into account the difference between the traits distribution in both datasets. We find that the accuracy of classifying age and gender, expressed by the area under the ROC curve (AUC), are 77% and 84% respectively for predictions based on Facebook Likes, and only degrade to 74% and 80% when based on search queries. On a US state-by-state basis we find a Pearson correlation of 0.72 for political views between the predicted scores and Gallup data, and 0.54 for affiliation with Judaism between predicted scores and data from the US Religious Landscape Survey. We conclude that it is indeed feasible to infer important demographic data of users from their query history based on labelled Likes data and believe that this approach could provide valuable information for personalization and monetization even in the absence of demographic data.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {131–140},
numpages = {10},
keywords = {personalized search, user demographics, social networks},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488402,
author = {Borodin, Allan and Braverman, Mark and Lucier, Brendan and Oren, Joel},
title = {Strategyproof Mechanisms for Competitive Influence in Networks},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488402},
doi = {10.1145/2488388.2488402},
abstract = {Motivated by applications to word-of-mouth advertising, we consider a game-theoretic scenario in which competing advertisers want to target initial adopters in a social network. Each advertiser wishes to maximize the resulting cascade of influence, modeled by a general network diffusion process. However, competition between products may adversely impact the rate of adoption for any given firm. The resulting framework gives rise to complex preferences that depend on the specifics of the stochastic diffusion model and the network topology.We study this model from the perspective of a central mechanism, such as a social networking platform, that can optimize seed placement as a service for the advertisers. We ask: given the reported demands of the competing firms, how should a mechanism choose seeds to maximize overall efficiency? Beyond the algorithmic problem, competition raises issues of strategic behaviour: rational agents should not be incentivized to underreport their budget demands.We show that when there are two players, the social welfare can be 2-approximated by a polynomial-time strategyproof mechanism. Our mechanism is defined recursively, randomizing the order in which advertisers are allocated seeds according to a particular greedy method. For three or more players, we demonstrate that under additional assumptions (satisfied by many existing models of influence spread) there exists a simpler strategyproof (e/e-1)-approximation mechanism; notably, this second mechanism is not necessarily strategyproof when there are only two players.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {141–150},
numpages = {10},
keywords = {influence spread, social networks, mechanism design},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488403,
author = {Bozzon, Alessandro and Brambilla, Marco and Ceri, Stefano and Mauri, Andrea},
title = {Reactive Crowdsourcing},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488403},
doi = {10.1145/2488388.2488403},
abstract = {An essential aspect for building effective crowdsourcing com- putations is the ability of "controlling the crowd", i.e. of dynamically adapting the behaviour of the crowdsourcing systems as response to the quantity and quality of completed tasks or to the availability and reliability of performers. Most crowdsourcing systems only provide limited and predefined controls; in contrast, we present an approach to crowdsourcing which provides fine-level, powerful and flexible controls. We model each crowdsourcing application as composition of elementary task types and we progressively transform these high level specifications into the features of a reactive execution environment that supports task planning, assignment and completion as well as performer monitoring and exclusion. Controls are specified as active rules on top of data structures which are derived from the model of the application; rules can be added, dropped or modified, thus guaranteeing maximal flexibility with limited effort.We also report on our prototype platform that implements the proposed framework and we show the results of our experimentations with different rule sets, demonstrating how simple changes to the rules can substantially affect time, effort and quality involved in crowdsourcing activities.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {153–164},
numpages = {12},
keywords = {control, reactive rules, crowdsourcing, social computation},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488404,
author = {Budak, Ceren and Agrawal, Rakesh},
title = {On Participation in Group Chats on Twitter},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488404},
doi = {10.1145/2488388.2488404},
abstract = {The success of a group depends on continued participation of its members through time. We study the factors that affect continued user participation in the context of educational Twitter chats. To predict whether a user that attended her first session in a particular Twitter chat group will return to the group, we build 5F Model that captures five different factors: individual initiative, group characteristics, perceived receptivity, linguistic affinity and geographical proximity. Through statistical data analysis of thirty Twitter chats over a two year period as well as a survey study, our work provides many insights about group dynamics in Twitter chats. We show similarities between Twitter chats and traditional groups such as the importance of social inclusion and linguistic similarity while also identifying important distinctions such as the insignificance of geographical proximity. We also show that informational support is more important than emotional support in educational Twitter chats, but this does not reduce the sense of community as suggested in earlier studies.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {165–176},
numpages = {12},
keywords = {information overload, ostracism, speech codes theory, online communities, twitter chats, group dynamics, mixed methods},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488405,
author = {Canali, Davide and Balzarotti, Davide and Francillon, Aur\'{e}lien},
title = {The Role of Web Hosting Providers in Detecting Compromised Websites},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488405},
doi = {10.1145/2488388.2488405},
abstract = {Compromised websites are often used by attackers to deliver malicious content or to host phishing pages designed to steal private information from their victims. Unfortunately, most of the targeted websites are managed by users with little security background - often unable to detect this kind of threats or to afford an external professional security service.In this paper we test the ability of web hosting providers to detect compromised websites and react to user complaints. We also test six specialized services that provide security monitoring of web pages for a small fee.During a period of 30 days, we hosted our own vulnerable websites on 22 shared hosting providers, including 12 of the most popular ones. We repeatedly ran five different attacks against each of them. Our tests included a bot-like infection, a drive-by download, the upload of malicious files, an SQL injection stealing credit card numbers, and a phishing kit for a famous American bank. In addition, we also generated traffic from seemingly valid victims of phishing and drive-by download sites. We show that most of these attacks could have been detected by free network or file analysis tools. After 25 days, if no malicious activity was detected, we started to file abuse complaints to the providers. This allowed us to study the reaction of the web hosting providers to both real and bogus complaints.The general picture we drew from our study is quite alarming. The vast majority of the providers, or "add-on" security monitoring services, are unable to detect the most simple signs of malicious activity on hosted websites.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {177–188},
numpages = {12},
keywords = {web security, shared web hosting},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488406,
author = {Carrascal, Juan Pablo and Riederer, Christopher and Erramilli, Vijay and Cherubini, Mauro and de Oliveira, Rodrigo},
title = {Your Browsing Behavior for a Big Mac: Economics of Personal Information Online},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488406},
doi = {10.1145/2488388.2488406},
abstract = {Most online service providers offer free services to users and in part, these services collect and monetize personally identifiable information (PII), primarily via targeted advertisements. Against this backdrop of economic exploitation of PII, it is vital to understand the value that users put to their own PII. Although studies have tried to discover how users value their privacy, little is known about how users value their PII while browsing, or the exploitation of their PII. Extracting valuations of PII from users is non-trivial - surveys cannot be relied on as they do not gather information of the context where PII is being released, thus reducing validity of answers. In this work, we rely on refined Experience Sampling - a data collection method that probes users to valuate their PII at the time and place where it was generated in order to minimize retrospective recall and hence increase measurement validity. For obtaining an honest valuation of PII, we use a reverse second price auction. We developed a web browser plugin and had 168 users - living in Spain - install and use this plugin for 2 weeks in order to extract valuations of PII in different contexts.We found that users value items of their online browsing history for about ∈7 (~10USD), and they give higher valuations to their offline PII, such as age and address (about 25∈ or ~36USD). When it comes to PII shared in specific online services, users value information pertaining to financial transactions and social network interactions more than activities like search and shopping. No significant distinction was found between valuations of different quantities of PII (e.g. one vs. 10 search keywords), but deviation was found between types of PII (e.g. photos vs. keywords). Finally, the users' preferred goods for exchanging their PII included money and improvements in service, followed by getting more free services and targeted advertisements.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {189–200},
numpages = {12},
keywords = {pii, privacy, economics of personal information, experience sampling},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488407,
author = {Chen, Ying and Xu, Heng and Zhou, Yilu and Zhu, Sencun},
title = {Is This App Safe for Children? A Comparison Study of Maturity Ratings on Android and IOS Applications},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488407},
doi = {10.1145/2488388.2488407},
abstract = {There is a rising concern among parents who have experienced unreliable content maturity ratings for mobile applications (apps) that result in inappropriate risk exposure for their children and adolescents. In reality, there is no consistent maturity rating policy for mobile applications. The maturity ratings of Android apps are provided purely by developers' self-disclosure and are rarely verified. While Apple's iOS app ratings are considered to be more accurate, they can also be inconsistent with Apple's published policies. To address these issues, this research aims to systematically uncover the extent and severity of unreliable maturity ratings for mobile apps. Specifically, we develop mechanisms to verify the maturity ratings of mobile apps and investigate possible reasons behind the incorrect ratings. We believe that our findings have important implications for platform providers (e.g., Google or Apple) as well as for regulatory bodies and application developers.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {201–212},
numpages = {12},
keywords = {children safety, ios apps, maturity rating, mobile app, privacy, android apps, application permissions},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488408,
author = {Christin, Nicolas},
title = {Traveling the Silk Road: A Measurement Analysis of a Large Anonymous Online Marketplace},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488408},
doi = {10.1145/2488388.2488408},
abstract = {We perform a comprehensive measurement analysis of Silk Road, an anonymous, international online marketplace that operates as a Tor hidden service and uses Bitcoin as its exchange currency. We gather and analyze data over eight months between the end of 2011 and 2012, including daily crawls of the marketplace for nearly six months in 2012. We obtain a detailed picture of the type of goods sold on Silk Road, and of the revenues made both by sellers and Silk Road operators. Through examining over 24,400 separate items sold on the site, we show that Silk Road is overwhelmingly used as a market for controlled substances and narcotics, and that most items sold are available for less than three weeks. The majority of sellers disappears within roughly three months of their arrival, but a core of 112 sellers has been present throughout our measurement interval. We evaluate the total revenue made by all sellers, from public listings, to slightly over USD 1.2 million per month; this corresponds to about USD 92,000 per month in commissions for the Silk Road operators. We further show that the marketplace has been operating steadily, with daily sales and number of sellers overall increasing over our measurement interval. We discuss economic and policy implications of our analysis and results, including ethical considerations for future research in this area.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {213–224},
numpages = {12},
keywords = {anonymity, online crime, electronic commerce},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@dataset{10.1145/review-2488388.2488408_R49768,
author = {Goodman, Brian D.},
title = {Review ID:R49768 for DOI: 10.1145/2488388.2488408},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/review-2488388.2488408_R49768}
}

@inproceedings{10.1145/2488388.2488409,
author = {Cook, James and Kenthapadi, Krishnaram and Mishra, Nina},
title = {Group Chats on Twitter},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488409},
doi = {10.1145/2488388.2488409},
abstract = {We report on a new kind of group conversation on Twitter that we call a group chat. These chats are periodic, synchronized group conversations focused on specific topics and they exist at a massive scale. The groups and the members of these groups are not explicitly known. Rather, members agree on a hashtag and a meeting time (e.g, 3pm Pacific Time every Wednesday) to discuss a subject of interest. Topics of these chats are numerous and varied. Some are support groups, for example, post-partum depression and mood disorder groups. Others are about a passionate interest: topics include skiing, photography, movies, wine and foodie communities. We develop a definition of a group that is inspired by how sociologists define groups and present an algorithm for discovering groups. We prove that our algorithms find all groups under certain assumptions. While these groups are of course known to the people who participate in the discussions, what we do not believe is known is the scale and variety of groups. We provide some insight into the nature of these groups based on over two years of tweets. Finally, we show that group chats are a growing phenomenon on Twitter and hope that reporting their existence propels their growth even further.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {225–236},
numpages = {12},
keywords = {groups, online communities, periodic time series},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488410,
author = {Cook, James and Fabrikant, Alex and Hassidim, Avinatan},
title = {How to Grow More Pairs: Suggesting Review Targets for Comparison-Friendly Review Ecosystems},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488410},
doi = {10.1145/2488388.2488410},
abstract = {We consider the algorithmic challenges behind a novel interface that simplifies consumer research of online reviews by surfacing relevant comparable review bundles: reviews for two or more of the items being researched, all generated in similar enough circumstances to provide for easy comparison. This can be reviews by the same reviewer, or by the same demographic category of reviewer, or reviews focusing on the same aspect of the items. But such an interface will work only if the review ecosystem often has comparable review bundles for common research tasks.Here, we develop and evaluate practical algorithms for suggesting additional review targets to reviewers to maximize comparable pair coverage, the fraction of co-researched pairs of items that have both been reviewed by the same reviewer (or more generally are comparable in one of several ways). We show the exact problem and many subcases to be intractable, and give a greedy online, linear-time 2-approximation for a very general setting, and an offline 1.583-approximation for a narrower setting. We evaluate the algorithms on the Google+ Local reviews dataset, yielding more than 10x gain in pair coverage from six months of simulated replacement of existing reviews by suggested reviews. Even allowing for 90% of reviewers ignoring the suggestions, the pair coverage grows more than 2x in the simulation. To explore other parts of the parameter space, we also evaluate the algorithms on synthetic models.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {237–248},
numpages = {12},
keywords = {graphs, algorithms, reviews, comparing},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488411,
author = {Cornolti, Marco and Ferragina, Paolo and Ciaramita, Massimiliano},
title = {A Framework for Benchmarking Entity-Annotation Systems},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488411},
doi = {10.1145/2488388.2488411},
abstract = {In this paper we design and implement a benchmarking framework for fair and exhaustive comparison of entity-annotation systems. The framework is based upon the definition of a set of problems related to the entity-annotation task, a set of measures to evaluate systems performance, and a systematic comparative evaluation involving all publicly available datasets, containing texts of various types such as news, tweets and Web pages. Our framework is easily-extensible with novel entity annotators, datasets and evaluation measures for comparing systems, and it has been released to the public as open source. We use this framework to perform the first extensive comparison among all available entity annotators over all available datasets, and draw many interesting conclusions upon their efficiency and effectiveness. We also draw conclusions between academic versus commercial annotators.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {249–260},
numpages = {12},
keywords = {wikipedia},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488412,
author = {Crescenzi, Valter and Merialdo, Paolo and Qiu, Disheng},
title = {A Framework for Learning Web Wrappers from the Crowd},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488412},
doi = {10.1145/2488388.2488412},
abstract = {The development of solutions to scale the extraction of data from Web sources is still a challenging issue. High accuracy can be achieved by supervised approaches but the costs of training data, i.e., annotations over a set of sample pages, limit their scalability. Crowd sourcing platforms are making the manual annotation process more affordable. However, the tasks demanded to these platforms should be extremely simple, to be performed by non-expert people, and their number should be minimized, to contain the costs. We introduce a framework to support a supervised wrapper inference system with training data generated by the crowd. Training data are labeled values generated by means of membership queries, the simplest form of queries, posed to the crowd. We show that the costs of producing the training data are strongly affected by the expressiveness of the wrapper formalism and by the choice of the training set. Traditional supervised wrapper inference approaches use a statically defined formalism, assuming it is able to express the wrapper. Conversely, we present an inference algorithm that dynamically chooses the expressiveness of the wrapper formalism and actively selects the training set, while minimizing the number of membership queries to the crowd. We report the results of experiments on real web sources to confirm the effectiveness and the feasibility of the approach.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {261–272},
numpages = {12},
keywords = {active learning, crowdsourcing, wrapper generation},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488413,
author = {Czeskis, Alexei and Moshchuk, Alexander and Kohno, Tadayoshi and Wang, Helen J.},
title = {Lightweight Server Support for Browser-Based CSRF Protection},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488413},
doi = {10.1145/2488388.2488413},
abstract = {Cross-Site Request Forgery (CSRF) attacks are one of the top threats on the web today. These attacks exploit ambient authority in browsers (eg cookies, HTTP authentication state), turning them into confused deputies and causing undesired side effects on vulnerable web sites. Existing defenses against CSRFs fall short in their coverage and/or ease of deployment. In this paper, we present a browser/server solution, Allowed Referrer Lists (ARLs), that addresses the root cause of CSRFs and removes ambient authority for participating web sites that want to be resilient to CSRF attacks. Our solution is easy for web sites to adopt and does not affect any functionality on non-participating sites. We have implemented our design in Firefox and have evaluated it with real-world sites. We found that ARLs successfully block CSRF attacks, are simpler to implement than existing defenses, and do not significantly impact browser performance.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {273–284},
numpages = {12},
keywords = {authentication, web browser, xsrf, csrf, authorization},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488414,
author = {Dalvi, Nilesh and Dasgupta, Anirban and Kumar, Ravi and Rastogi, Vibhor},
title = {Aggregating Crowdsourced Binary Ratings},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488414},
doi = {10.1145/2488388.2488414},
abstract = {In this paper we analyze a crowdsourcing system consisting of a set of users and a set of binary choice questions. Each user has an unknown, fixed, reliability that determines the user's error rate in answering questions. The problem is to determine the truth values of the questions solely based on the user answers. Although this problem has been studied extensively, theoretical error bounds have been shown only for restricted settings: when the graph between users and questions is either random or complete. In this paper we consider a general setting of the problem where the user--question graph can be arbitrary. We obtain bounds on the error rate of our algorithm and show it is governed by the expansion of the graph. We demonstrate, using several synthetic and real datasets, that our algorithm outperforms the state of the art.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {285–294},
numpages = {10},
keywords = {mechanical turk, spectral methods, crowdsourcing},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488415,
author = {Dalvi, Nilesh and Rastogi, Vibhor and Dasgupta, Anirban and Das Sarma, Anish and Sarlos, Tamas},
title = {Optimal Hashing Schemes for Entity Matching},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488415},
doi = {10.1145/2488388.2488415},
abstract = {In this paper, we consider the problem of devising blocking schemes for entity matching. There is a lot of work on blocking techniques for supporting various kinds of predicates, e.g. exact matches, fuzzy string-similarity matches, and spatial matches. However, given a complex entity matching function in the form of a Boolean expression over several such predicates, we show that it is an important and non-trivial problem to combine the individual blocking techniques into an efficient blocking scheme for the entity matching function, a problem that has not been studied previously.In this paper, we make fundamental contributions to this problem. We consider an abstraction for modeling complex entity matching functions as well as blocking schemes. We present several results of theoretical and practical interest for the problem. We show that in general, the problem of computing the optimal blocking strategy is NP-hard in the size of the DNF formula describing the matching function. We also present several algorithms for computing the exact optimal strategies (with exponential complexity, but often feasible in practice) as well as fast approximation algorithms. We experimentally demonstrate over commercially used rule-based matching systems over real datasets at Yahoo!, as well as synthetic datasets, that our blocking strategies can be an order of magnitude faster than the baseline methods, and our algorithms can efficiently find good blocking strategies.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {295–306},
numpages = {12},
keywords = {blocking, hashing, entity matching},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488416,
author = {Danescu-Niculescu-Mizil, Cristian and West, Robert and Jurafsky, Dan and Leskovec, Jure and Potts, Christopher},
title = {No Country for Old Members: User Lifecycle and Linguistic Change in Online Communities},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488416},
doi = {10.1145/2488388.2488416},
abstract = {Vibrant online communities are in constant flux. As members join and depart, the interactional norms evolve, stimulating further changes to the membership and its social dynamics. Linguistic change --- in the sense of innovation that becomes accepted as the norm --- is essential to this dynamic process: it both facilitates individual expression and fosters the emergence of a collective identity.We propose a framework for tracking linguistic change as it happens and for understanding how specific users react to these evolving norms. By applying this framework to two large online communities we show that users follow a determined two-stage lifecycle with respect to their susceptibility to linguistic change: a linguistically innovative learning phase in which users adopt the language of the community followed by a conservative phase in which users stop changing and the evolving community norms pass them by.Building on this observation, we show how this framework can be used to detect, early in a user's career, how long she will stay active in the community. Thus, this work has practical significance for those who design and maintain online communities. It also yields new theoretical insights into the evolution of linguistic norms and the complex interplay between community-level and individual-level linguistic change.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {307–318},
numpages = {12},
keywords = {user abandonment, social influence, conventions, community norms, reviews, linguistic change, lifecycle, language},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488417,
author = {Dasgupta, Anirban and Ghosh, Arpita},
title = {Crowdsourced Judgement Elicitation with Endogenous Proficiency},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488417},
doi = {10.1145/2488388.2488417},
abstract = {Crowdsourcing is now widely used to replace judgement or evaluation by an expert authority with an aggregate evaluation from a number of non-experts, in applications ranging from rating and categorizing online content all the way to evaluation of student assignments in massively open online courses (MOOCs) via peer grading. A key issue in these settings, where direct monitoring of both effort and accuracy is infeasible, is incentivizing agents in the 'crowd' to put in effort to make good evaluations, as well as to truthfully report their evaluations. We study the design of mechanisms for crowdsourced judgement elicitation when workers strategically choose both their reports and the effort they put into their evaluations. This leads to a new family of information elicitation problems with unobservable ground truth, where an agent's proficiency--- the probability with which she correctly evaluates the underlying ground truth--- is endogenously determined by her strategic choice of how much effort to put into the task.Our main contribution is a simple, new, mechanism for binary information elicitation for multiple tasks when agents have endogenous proficiencies, with the following properties: (i) Exerting maximum effort followed by truthful reporting of observations is a Nash equilibrium. (ii) This is the equilibrium with maximum payoff to all agents, even when agents have different maximum proficiencies, can use mixed strategies, and can choose a different strategy for each of their tasks. Our information elicitation mechanism requires only minimal bounds on the priors, asks agents to only report their own evaluations, and does not require any conditions on a diverging number of agent reports per task to achieve its incentive properties. The main idea behind our mechanism is to use the presence of multiple tasks and ratings to estimate a reporting statistic to identify and penalize low-effort agreement--- the mechanism rewards agents for agreeing with another 'reference' report on the same task, but also penalizes for blind agreement by subtracting out this statistic term, designed so that agents obtain rewards only when they put in effort into their observations.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {319–330},
numpages = {12},
keywords = {mechanism design, crowdsourcing, game theory, information elicitation, peer prediction},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488418,
author = {Dave, Kushal S. and Vaingankar, Vishal and Kolar, Sumanth and Varma, Vasudeva},
title = {Timespent Based Models for Predicting User Retention},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488418},
doi = {10.1145/2488388.2488418},
abstract = {Content discovery is fast becoming the preferred tool for user engagement on the web. Discovery allows users to get educated and entertained about their topics of interest. StumbleUpon is the largest personalized content discovery engine on the Web, delivering more than 1 billion personalized recommendations per month. As a recommendation system one of the primary metrics we track is whether the user returns (retention) to use the product after their initial experience (session) with StumbleUpon.In this paper, we attempt to address the problem of predicting user retention based on the user's previous sessions. The paper first explores the different user and content features that are helpful in predicting user retention. This involved mapping the user and the user's recommendations (stumbles) in a descriptive feature space such as the time-spent by user, number of stumbles, and content features of the recommendations. To model the diversity in user behaviour, we also generated normalized features that account for the user's speed of stumbling. Using these features, we built a decision tree classifier to predict retention. We find that a model that uses both the user and content features achieves higher prediction accuracy than a model that uses the two features separately. Further, we used information theoretical analysis to find a subset of recommendations that are most indicative of user retention. A classifier trained on this subset of recommendations achieves the highest prediction accuracy. This indicates that not every recommendation seen by the user is predictive of whether the user will be retained; instead, a subset of most informative recommendations is more useful in predicting retention.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {331–342},
numpages = {12},
keywords = {time spent, retention, stumbleupon},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488419,
author = {de Alfaro, Luca and Shavlovsky, Michael},
title = {Attributing Authorship of Revisioned Content},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488419},
doi = {10.1145/2488388.2488419},
abstract = {A considerable portion of web content, from wikis to collaboratively edited documents, to code posted online, is revisioned. We consider the problem of attributing authorship to such revisioned content, and we develop scalable attribution algorithms that can be applied to very large bodies of revisioned content, such as the English Wikipedia.Since content can be deleted, only to be later re-inserted, we introduce a notion of authorship that requires comparing each new revision with the entire set of past revisions. For each portion of content in the newest revision, we search the entire history for content matches that are statistically unlikely to occur spontaneously, thus denoting common origin. We use these matches to compute the earliest possible attribution of each word (or each token) of the new content. We show that this "earliest plausible attribution" can be computed efficiently via compact summaries of the past revision history. This leads to an algorithm that runs in time proportional to the sum of the size of the most recent revision, and the total amount of change (edit work) in the revision history. This amount of change is typically much smaller than the total size of all past revisions. The resulting algorithm can scale to very large repositories of revisioned content, as we show via experimental data over the English Wikipedia.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {343–354},
numpages = {12},
keywords = {wikipedia, authorship, revisioned content},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488420,
author = {Del Corro, Luciano and Gemulla, Rainer},
title = {ClausIE: Clause-Based Open Information Extraction},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488420},
doi = {10.1145/2488388.2488420},
abstract = {We propose ClausIE, a novel, clause-based approach to open information extraction, which extracts relations and their arguments from natural language text. ClausIE fundamentally differs from previous approaches in that it separates the detection of ``useful'' pieces of information expressed in a sentence from their representation in terms of extractions. In more detail, ClausIE exploits linguistic knowledge about the grammar of the English language to first detect clauses in an input sentence and to subsequently identify the type of each clause according to the grammatical function of its constituents. Based on this information, ClausIE is able to generate high-precision extractions; the representation of these extractions can be flexibly customized to the underlying application. ClausIE is based on dependency parsing and a small set of domain-independent lexica, operates sentence by sentence without any post-processing, and requires no training data (whether labeled or unlabeled). Our experimental study on various real-world datasets suggests that ClausIE obtains higher recall and higher precision than existing approaches, both on high-quality text as well as on noisy text as found in the web.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {355–366},
numpages = {12},
keywords = {open information extraction, relation extraction},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488421,
author = {Difallah, Djellel Eddine and Demartini, Gianluca and Cudr\'{e}-Mauroux, Philippe},
title = {Pick-a-Crowd: Tell Me What You like, and i'll Tell You What to Do},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488421},
doi = {10.1145/2488388.2488421},
abstract = {Crowdsourcing allows to build hybrid online platforms that combine scalable information systems with the power of human intelligence to complete tasks that are difficult to tackle for current algorithms. Examples include hybrid database systems that use the crowd to fill missing values or to sort items according to subjective dimensions such as picture attractiveness. Current approaches to Crowdsourcing adopt a pull methodology where tasks are published on specialized Web platforms where workers can pick their preferred tasks on a first-come-first-served basis. While this approach has many advantages, such as simplicity and short completion times, it does not guarantee that the task is performed by the most suitable worker. In this paper, we propose and extensively evaluate a different Crowdsourcing approach based on a push methodology. Our proposed system carefully selects which workers should perform a given task based on worker profiles extracted from social networks. Workers and tasks are automatically matched using an underlying categorization structure that exploits entities extracted from the task descriptions on one hand, and categories liked by the user on social platforms on the other hand. We experimentally evaluate our approach on tasks of varying complexity and show that our push methodology consistently yield better results than usual pull strategies.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {367–374},
numpages = {8},
keywords = {expert profiling, crowdsourcing, social network},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488422,
author = {Dong, Xin Luna and Srivastava, Divesh},
title = {Compact Explanation of Data Fusion Decisions},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488422},
doi = {10.1145/2488388.2488422},
abstract = {Despite the abundance of useful information on the Web, different Web sources often provide conflicting data, some being out-of-date, inaccurate, or erroneous. Data fusion aims at resolving conflicts and finding the truth. Advanced fusion techniques apply iterative MAP (Maximum A Posteriori) analysis that reasons about trustworthiness of sources and copying relationships between them. Providing explanations for such decisions is important for a better understanding, but can be extremely challenging because of the complexity of the analysis during decision making.This paper proposes two types of explanations for data-fusion results: snapshot explanations take the provided data and any other decision inferred from the data as evidence and provide a high-level understanding of a fusion decision; comprehensive explanations take only the data as evidence and provide an in-depth understanding of a fusion decision. We propose techniques that can efficiently generate correct and compact explanations. Experimental results show that (1) we generate correct explanations, (2) our techniques can significantly reduce the sizes of the explanations, and (3) we can generate the explanations efficiently.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {379–390},
numpages = {12},
keywords = {copy detection, data fusion, explanation},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488423,
author = {Dror, Gideon and Maarek, Yoelle and Mejer, Avihai and Szpektor, Idan},
title = {From Query to Question in One Click: Suggesting Synthetic Questions to Searchers},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488423},
doi = {10.1145/2488388.2488423},
abstract = {In Web search, users may remain unsatisfied for several reasons: the search engine may not be effective enough or the query might not reflect their intent. Years of research focused on providing the best user experience for the data available to the search engine. However, little has been done to address the cases in which relevant content for the specific user need has not been posted on the Web yet. One obvious solution is to directly ask other users to generate the missing content using Community Question Answering services such as Yahoo! Answers or Baidu Zhidao. However, formulating a full-fledged question after having issued a query requires some effort. Some previous work proposed to automatically generate natural language questions from a given query, but not for scenarios in which a searcher is presented with a list of questions to choose from. We propose here to generate synthetic questions that can actually be clicked by the searcher so as to be directly posted as questions on a Community Question Answering service. This imposes new constraints, as questions will be actually shown to searchers, who will not appreciate an awkward style or redundancy. To this end, we introduce a learning-based approach that improves not only the relevance of the suggested questions to the original query, but also their grammatical correctness. In addition, since queries are often underspecified and ambiguous, we put a special emphasis on increasing the diversity of suggestions via a novel diversification mechanism. We conducted several experiments to evaluate our approach by comparing it to prior work. The experiments show that our algorithm improves question quality by 14% over prior work and that adding diversification reduced redundancy by 55%.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {391–402},
numpages = {12},
keywords = {question generation, community-based question answering},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488424,
author = {Fernquist, Jennifer and Chi, Ed H.},
title = {Perception and Understanding of Social Annotations in Web Search},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488424},
doi = {10.1145/2488388.2488424},
abstract = {As web search increasingly becomes reliant on social signals, it is imperative for us to understand the effect of these signals on users' behavior. There are multiple ways in which social signals can be used in search: (a) to surface and rank important social content; (b) to signal to users which results are more trustworthy and important by placing annotations on search results. We focus on the latter problem of understanding how social annotations affect user behavior.In previous work, through eyetracking research we learned that users do not generally seem to fixate on social annotations when they are placed at the bottom of the search result block, with 11% probability of fixation [22]. A second eyetracking study showed that placing the annotation on top of the snippet block might mitigate this issue [22], but this study was conducted using mock-ups and with expert searchers.In this paper, we describe a study conducted with a new eyetracking mix-method using a live traffic search engine with the suggested design changes on real users using the same experimental procedures. The study comprised of 11 subjects with an average of 18 tasks per subject using an eyetrace-assisted retrospective think-aloud protocol. Using a funnel analysis, we found that users are indeed more likely to notice the annotations with a 60% probability of fixation (if the annotation was in view). Moreover, we found no learning effects across search sessions but found significant differences in query types, with subjects having a lower chance of fixating on annotations for queries in the news category. In the interview portion of the study, users reported interesting "wow" moments as well as usefulness in recalling or re-finding content previously shared by oneself or friends. The results not only shed light on how social annotations should be designed in search engines, but also how users make use of social annotations to make decisions about which pages are useful and potentially trustworthy.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {403–412},
numpages = {10},
keywords = {eyetracking, social search, annotation, user study},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488425,
author = {Gal\'{a}rraga, Luis Antonio and Teflioudi, Christina and Hose, Katja and Suchanek, Fabian},
title = {AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488425},
doi = {10.1145/2488388.2488425},
abstract = {Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive Logic Programming (ILP) can be used to mine logical rules from the KB. These rules can help deduce and add missing knowledge to the KB. While ILP is a mature field, mining logical rules from KBs is different in two aspects: First, current rule mining systems are easily overwhelmed by the amount of data (state-of-the art systems cannot even run on today's KBs). Second, ILP usually requires counterexamples. KBs, however, implement the open world assumption (OWA), meaning that absent data cannot be used as counterexamples. In this paper, we develop a rule mining model that is explicitly tailored to support the OWA scenario. It is inspired by association rule mining and introduces a novel measure for confidence. Our extensive experiments show that our approach outperforms state-of-the-art approaches in terms of precision and coverage. Furthermore, our system, AMIE, mines rules orders of magnitude faster than state-of-the-art approaches.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {413–422},
numpages = {10},
keywords = {rule mining, ilp, inductive logic programming},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488426,
author = {Gao, Sidan and Anyanwu, Kemafor},
title = {PrefixSolve: Efficiently Solving Multi-Source Multi-Destination Path Queries on RDF Graphs by Sharing Suffix Computations},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488426},
doi = {10.1145/2488388.2488426},
abstract = {Uncovering the "nature" of the connections between a set of entities e.g. passengers on a flight and organizations on a watchlist can be viewed as a Multi-Source Multi-Destination (MSMD) Path Query problem on labeled graph data models such as RDF. Using existing graph-navigational path finding techniques to solve MSMD problems will require queries to be decomposed into multiple single-source or destination path subqueries, each of which is solved independently. Navigational techniques on disk-resident graphs typically generate very poor I/O access patterns for large, disk-resident graphs and for MSMD path queries, such poor access patterns may be repeated if common graph exploration steps exist across subqueries.In this paper, we propose an optimization technique for general MSMD path queries that generalizes an efficient algebraic approach for solving a variety of single-source path problems. The generalization enables holistic evaluation of MSMD path queries without the need for query decomposition. We present a conceptual framework for sharing computation in the algebraic framework that is based on "suffix equivalence". Suffix equivalence amongst subqueries captures the fact that multiple subqueries with different prefixes can share a suffix and as such share the computation of shared suffixes, which allows prefix path computations to share common suffix path computations. This approach offers orders of magnitude better performance than current existing techniques as demonstrated by a comprehensive experimental evaluation over real and synthetic datasets.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {423–434},
numpages = {12},
keywords = {work sharing, suffix equivalence, msmd path query, rdf},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488427,
author = {Gilad, Yossi and Herzberg, Amir},
title = {When Tolerance Causes Weakness: The Case of Injection-Friendly Browsers},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488427},
doi = {10.1145/2488388.2488427},
abstract = {We present a practical off-path TCP-injection attack for connections between current, non-buggy browsers and web-servers. The attack allows web-cache poisoning with malicious objects; these objects can be cached for long time period, exposing any user of that cache to XSS, CSRF and phishing attacks.In contrast to previous TCP-injection attacks, we assume neither vulnerabilities such as client-malware nor predictable choice of client port or IP-ID. We only exploit subtle details of HTTP and TCP specifications, and features of legitimate (and common) browser implementations. An empirical evaluation of our techniques with current versions of browsers shows that connections with popular websites are vulnerable. Our attack is modular, and its modules may improve other off-path attacks on TCP communication.We present practical patches against the attack; however, the best defense is surely adoption of TLS, that ensures security even against the stronger Man-in-the-Middle attacker.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {435–446},
numpages = {12},
keywords = {browser security, off-path attacks, web and network security},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488428,
author = {Goga, Oana and Lei, Howard and Parthasarathi, Sree Hari Krishnan and Friedland, Gerald and Sommer, Robin and Teixeira, Renata},
title = {Exploiting Innocuous Activity for Correlating Users across Sites},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488428},
doi = {10.1145/2488388.2488428},
abstract = {We study how potential attackers can identify accounts on different social network sites that all belong to the same user, exploiting only innocuous activity that inherently comes with posted content. We examine three specific features on Yelp, Flickr, and Twitter: the geo-location attached to a user's posts, the timestamp of posts, and the user's writing style as captured by language models. We show that among these three features the location of posts is the most powerful feature to identify accounts that belong to the same user in different sites. When we combine all three features, the accuracy of identifying Twitter accounts that belong to a set of Flickr users is comparable to that of existing attacks that exploit usernames. Our attack can identify 37% more accounts than using usernames when we instead correlate Yelp and Twitter. Our results have significant privacy implications as they present a novel class of attacks that exploit users' tendency to assume that, if they maintain different personas with different names, the accounts cannot be linked together; whereas we show that the posts themselves can provide enough information to correlate the accounts.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {447–458},
numpages = {12},
keywords = {language, location, online social networks, privacy, account correlation, geotags, user profiles},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488429,
author = {Goldstein, Daniel G. and McAfee, R. Preston and Suri, Siddharth},
title = {The Cost of Annoying Ads},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488429},
doi = {10.1145/2488388.2488429},
abstract = {Display advertisements vary in the extent to which they annoy users. While publishers know the payment they receive to run annoying ads, little is known about the cost such ads incur due to user abandonment. We conducted a two-experiment investigation to analyze ad features that relate to annoyingness and to put a monetary value on the cost of annoying ads. The first experiment asked users to rate and comment on a large number of ads taken from the Web. This allowed us to establish sets of annoying and innocuous ads for use in the second experiment, in which users were given the opportunity to categorize emails for a per-message wage and quit at any time. Participants were randomly assigned to one of three different pay rates and also randomly assigned to categorize the emails in the presence of no ads, annoying ads, or innocuous ads. Since each email categorization constituted an impression, this design, inspired by Toomim et al., allowed us to determine how much more one must pay a person to generate the same number of impressions in the presence of annoying ads compared to no ads or innocuous ads. We conclude by proposing a theoretical model which relates ad quality to publisher market share, illustrating how our empirical findings could affect the economics of Internet advertising.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {459–470},
numpages = {12},
keywords = {advertising, compensating differential, display, quality},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488430,
author = {Gollapalli, Sujatha Das and Caragea, Cornelia and Mitra, Prasenjit and Giles, C. Lee},
title = {Researcher Homepage Classification Using Unlabeled Data},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488430},
doi = {10.1145/2488388.2488430},
abstract = {A classifier that determines if a webpage is relevant to a specified set of topics comprises a key component for focused crawling. Can a classifier that is tuned to perform well on training datasets continue to filter out irrelevant pages in the face of changed content on the Web? We investigate this question in the context of researcher homepage crawling.We show experimentally that classifiers trained on existing datasets for homepage identification underperform while classifying "irrelevant" pages on current-day academic websites. As an alternative to obtaining datasets to retrain the classifier for the new content, we propose to use effectively unlimited amounts of unlabeled data readily available from these websites in a co-training scenario. To this end, we design novel URL-based features and use them in conjunction with content-based features as complementary views of the data to obtain remarkable improvements in accurately identifying homepages from the current-day university websites.In addition, we propose a novel technique for "learning a conforming pair of classifiers" using mini-batch gradient descent. Our algorithm seeks to minimize a loss (objective) function quantifying the difference in predictions from the two views afforded by co-training. We demonstrate that tuning the classifiers so that they make "similar" predictions on unlabeled data strongly corresponds to the effect achieved by co-training algorithms. We argue that this loss formulation provides insight into understanding the co-training process and can be used even in absence of a validation set.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {471–482},
numpages = {12},
keywords = {gradient descent, consensus maximization, co-training},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488431,
author = {Gonzalez, Roberto and Cuevas, Ruben and Motamedi, Reza and Rejaie, Reza and Cuevas, Angel},
title = {Google+ or Google-? Dissecting the Evolution of the New OSN in Its First Year},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488431},
doi = {10.1145/2488388.2488431},
abstract = {In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question that "whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter?".This paper tackles the above question by presenting a detailed characterization of G+ based on large scale measurements. We identify the main components of G+ structure, characterize the key features of their users and their evolution over time. We then conduct detailed analysis on the evolution of connectivity and activity among users in the largest connected component (LCC) of G+ structure, and compare their characteristics with other major OSNs. We show that despite the dramatic growth in the size of G+, the relative size of LCC has been decreasing and its connectivity has become less clustered. While the aggregate user activity has gradually increased, only a very small fraction of users exhibit any type of activity. To our knowledge, our study offers the most comprehensive characterization of G+ based on the largest collected data sets.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {483–494},
numpages = {12},
keywords = {evolution, characterization, osns, measurements, google+},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488432,
author = {Gorla, Jagadeesh and Lathia, Neal and Robertson, Stephen and Wang, Jun},
title = {Probabilistic Group Recommendation via Information Matching},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488432},
doi = {10.1145/2488388.2488432},
abstract = {Increasingly, web recommender systems face scenarios where they need to serve suggestions to groups of users; for example, when families share e-commerce or movie rental web accounts. Research to date in this domain has proposed two approaches: computing recommendations for the group by merging any members' ratings into a single profile, or computing ranked recommendations for each individual that are then merged via a range of heuristics. In doing so, none of the past approaches reason on the preferences that arise in individuals when they are members of a group. In this work, we present a probabilistic framework, based on the notion of information matching, for group recommendation. This model defines group relevance as a combination of the item's relevance to each user as an individual and as a member of the group; it can then seamlessly incorporate any group recommendation strategy in order to rank items for a set of individuals. We evaluate the model's efficacy at generating recommendations for both single individuals and groups using the MovieLens and MoviePilot data sets. In both cases, we compare our results with baselines and state-of-the-art collaborative filtering algorithms, and show that the model outperforms all others over a variety of ranking metrics.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {495–504},
numpages = {10},
keywords = {information matching, group recommendation, relevance ranking, probabilistic modelling},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488433,
author = {Gupta, Pankaj and Goel, Ashish and Lin, Jimmy and Sharma, Aneesh and Wang, Dong and Zadeh, Reza},
title = {WTF: The Who to Follow Service at Twitter},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488433},
doi = {10.1145/2488388.2488433},
abstract = {WTF ("Who to Follow") is Twitter's user recommendation service, which is responsible for creating millions of connections daily between users based on shared interests, common connections, and other related factors. This paper provides an architectural overview and shares lessons we learned in building and running the service over the past few years. Particularly noteworthy was our design decision to process the entire Twitter graph in memory on a single server, which significantly reduced architectural complexity and allowed us to develop and deploy the service in only a few months. At the core of our architecture is Cassovary, an open-source in-memory graph processing engine we built from scratch for WTF. Besides powering Twitter's user recommendations, Cassovary is also used for search, discovery, promoted products, and other services as well. We describe and evaluate a few graph recommendation algorithms implemented in Cassovary, including a novel approach based on a combination of random walks and SALSA. Looking into the future, we revisit the design of our architecture and comment on its limitations, which are presently being addressed in a second-generation system under development.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {505–514},
numpages = {10},
keywords = {graph processing, link prediction, hadoop},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488434,
author = {Guy, Ido and Avraham, Uri and Carmel, David and Ur, Sigalit and Jacovi, Michal and Ronen, Inbal},
title = {Mining Expertise and Interests from Social Media},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488434},
doi = {10.1145/2488388.2488434},
abstract = {The rising popularity of social media in the enterprise presents new opportunities for one of the organization's most important needs--expertise location. Social media data can be very useful for expertise mining due to the variety of existing applications, the rich metadata, and the diversity of user associations with content. In this work, we provide an extensive study that explores the use of social media to infer expertise within a large global organization. We examine eight different social media applications by evaluating the data they produce through a large user survey, with 670 enterprise social media users. We distinguish between two semantics that relate a user to a topic: expertise in the topic and interest in it and compare these two semantics across the different social media applications.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {515–526},
numpages = {12},
keywords = {expertise location, expert finding, social media, people search, interest mining, expert recommendation, enterprise, social software, social business, expert search, enterprise 2.0, web 2.0, social computing},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488435,
author = {Hannak, Aniko and Sapiezynski, Piotr and Molavi Kakhki, Arash and Krishnamurthy, Balachander and Lazer, David and Mislove, Alan and Wilson, Christo},
title = {Measuring Personalization of Web Search},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488435},
doi = {10.1145/2488388.2488435},
abstract = {Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it.In light of this situation, we make three contributions. First, we develop a methodology for measuring personalization in Web search results. While conceptually simple, there are numerous details that our methodology must handle in order to accurately attribute differences in search results to personalization. Second, we apply our methodology to 200 users on Google Web Search; we find that, on average, 11.7% of results show differences due to personalization, but that this varies widely by search query and by result ranking. Third, we investigate the causes of personalization on Google Web Search. Surprisingly, we only find measurable personalization as a result of searching with a logged in account and the IP address of the searching user. Our results are a first step towards understanding the extent and effects of personalization on Web search engines today.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {527–538},
numpages = {12},
keywords = {personalization, web search},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488436,
author = {Hardiman, Stephen J. and Katzir, Liran},
title = {Estimating Clustering Coefficients and Size of Social Networks via Random Walk},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488436},
doi = {10.1145/2488388.2488436},
abstract = {Online social networks have become a major force in today's society and economy. The largest of today's social networks may have hundreds of millions to more than a billion users. Such networks are too large to be downloaded or stored locally, even if terms of use and privacy policies were to permit doing so. This limitation complicates even simple computational tasks. One such task is computing the clustering coefficient of a network. Another task is to compute the network size (number of registered users) or a subpopulation size.The clustering coefficient, a classic measure of network connectivity, comes in two flavors, global and network average. In this work, we provide efficient algorithms for estimating these measures which (1) assume no prior knowledge about the network; and (2) access the network using only the publicly available interface. More precisely, this work provides three new estimation algorithms (a) the first external access algorithm for estimating the global clustering coefficient; (b) an external access algorithm that improves on the accuracy of previous network average clustering coefficient estimation algorithms; and (c) an improved external access network size estimation algorithm.The main insight offered by this work is that only a relatively small number of public interface calls are required to allow our algorithms to achieve a high accuracy estimation. Our approach is to view a social network as an undirected graph and use the public interface to retrieve a random walk. To estimate the clustering coefficient, the connectivity of each node in the random walk sequence is tested in turn. We show that the error of this estimation drops exponentially in the number of random walk steps. Another insight of this work is the fact that, although the proposed algorithms can be used to estimate the clustering coefficient of any undirected graph, they are particularly efficient on social network-like graphs. To improve the network size prior-art estimation algorithms, we count node collision one step before they actually occur. In our experiments we validate our algorithms on several publicly available social network datasets. Our results validate the theoretical claims and demonstrate the effectiveness of our algorithms.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {539–550},
numpages = {12},
keywords = {social network, sampling, estimation, clustering coefficient},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488437,
author = {Heinrich, Matthias and Gr\"{u}neberger, Franz Josef and Springer, Thomas and Gaedke, Martin},
title = {Exploiting Annotations for the Rapid Development of Collaborative Web Applications},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488437},
doi = {10.1145/2488388.2488437},
abstract = {Web application frameworks are a proven means to accelerate the development of interactive web applications. However, implementing collaborative real-time applications like Google Docs requires specific concurrency control services (i.e. document synchronization and conflict resolution) that are not included in prevalent general-purpose frameworks like jQuery or Knockout. Hence, developers have to get familiar with specific collaboration frameworks (e.g. ShareJS) which substantially increases the development effort. To ease the development of collaborative web applications, we propose a set of source code annotations representing a lightweight mechanism to introduce concurrency control services into mature web frameworks. Those annotations are interpreted at runtime by a dedicated collaboration engine to sync documents and resolve conflicts. We enhanced the general-purpose framework Knockout with a collaboration engine and conducted a developer study comparing our approach to a traditional concurrency control library. The evaluation results show that the effort to incorporate collaboration capabilities into a web application can be reduced by up to 40 percent using the annotation-based solution.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {551–560},
numpages = {10},
keywords = {web applications, shared editing, groupware, web engineering},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488438,
author = {Hollink, Laura and Mika, Peter and Blanco, Roi},
title = {Web Usage Mining with Semantic Analysis},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488438},
doi = {10.1145/2488388.2488438},
abstract = {Web usage mining has traditionally focused on the individual queries or query words leading to a web site or web page visit, mining patterns in such data. In our work, we aim to characterize websites in terms of the semantics of the queries that lead to them by linking queries to large knowledge bases on the Web. We demonstrate how to exploit such links for more effective pattern mining on query log data. We also show how such patterns can be used to qualitatively describe the differences between competing websites in the same domain and to quantitatively predict website abandonment.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {561–570},
numpages = {10},
keywords = {semantic analysis, query session, query log},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488439,
author = {Hsieh, Cho-Jui and Tiwari, Mitul and Agarwal, Deepak and Huang, Xinyi (Lisa) and Shah, Sam},
title = {Organizational Overlap on Social Networks and Its Applications},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488439},
doi = {10.1145/2488388.2488439},
abstract = {Online social networks have become important for networking, communication, sharing, and discovery. A considerable challenge these networks face is the fact that an online social network is partially observed because two individuals might know each other, but may not have established a connection on the site. Therefore, link prediction and recommendations are important tasks for any online social network. In this paper, we address the problem of computing edge affinity between two users on a social network, based on the users belonging to organizations such as companies, schools, and online groups. We present experimental insights from social network data on organizational overlap, a novel mathematical model to compute the probability of connection between two people based on organizational overlap, and experimental validation of this model based on real social network data. We also present novel ways in which the organization overlap model can be applied to link prediction and community detection, which in itself could be useful for recommending entities to follow and generating personalized news feed.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {571–582},
numpages = {12},
keywords = {social networks, community detection, organizational overlap, link prediction},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488440,
author = {Hsu, Bo-June (Paul) and Ottaviano, Giuseppe},
title = {Space-Efficient Data Structures for Top-<i>k</i> Completion},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488440},
doi = {10.1145/2488388.2488440},
abstract = {Virtually every modern search application, either desktop, web, or mobile, features some kind of query auto-completion. In its basic form, the problem consists in retrieving from a string set a small number of completions, i.e. strings beginning with a given prefix, that have the highest scores according to some static ranking. In this paper, we focus on the case where the string set is so large that compression is needed to fit the data structure in memory. This is a compelling case for web search engines and social networks, where it is necessary to index hundreds of millions of distinct queries to guarantee a reasonable coverage; and for mobile devices, where the amount of memory is limited.We present three different trie-based data structures to address this problem, each one with different space/time/complexity trade-offs. Experiments on large-scale datasets show that it is possible to compress the string sets, including the scores, down to spaces competitive with the gzip'ed data, while supporting efficient retrieval of completions at about a microsecond per completion.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {583–594},
numpages = {12},
keywords = {tries, top-k completion, compression, scored string sets},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488441,
author = {Hu, Liang and Cao, Jian and Xu, Guandong and Cao, Longbing and Gu, Zhiping and Zhu, Can},
title = {Personalized Recommendation via Cross-Domain Triadic Factorization},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488441},
doi = {10.1145/2488388.2488441},
abstract = {Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {595–606},
numpages = {12},
keywords = {cross-domain collaborative filtering, recommender system, triadic factorization},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488442,
author = {Hu, Xia and Tang, Jiliang and Gao, Huiji and Liu, Huan},
title = {Unsupervised Sentiment Analysis with Emotional Signals},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488442},
doi = {10.1145/2488388.2488442},
abstract = {The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications. It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving. Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words. Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals. In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {607–618},
numpages = {12},
keywords = {social media, sentiment classification, social correlation, sentiment analysis, Twitter, emoticon, emotional signals},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488443,
author = {Huang, Ting-Kai and Rahman, Md Sazzadur and Madhyastha, Harsha V. and Faloutsos, Michalis and Ribeiro, Bruno},
title = {An Analysis of Socware Cascades in Online Social Networks},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488443},
doi = {10.1145/2488388.2488443},
abstract = {Online social networks (OSNs) have become a popular new vector for distributing malware and spam, which we refer to as socware. Unlike email spam, which is sent by spammers directly to intended victims, socware cascades through OSNs as compromised users spread it to their friends. In this paper, we analyze data from the walls of roughly 3 million Facebook users over five months, with the goal of developing a better understanding of socware cascades.We study socware cascades to understand: (a) their spatio-temporal properties, (b) the underlying motivations and mechanisms, and (c) the social engineering tricks used to con users. First, we identify an evolving trend in which cascades appear to be throttling their rate of growth to evade detection, and thus, lasting longer. Second, our forensic investigation into the infrastructure that supports these cascades shows that, surprisingly, Facebook seems to be inadvertently enabling most cascades; 44% of cascades are disseminated via Facebook applications. At the same time, we observe large groups of synergistic Facebook apps (more than 144 groups of size 5 or more) that collaborate to support multiple cascades. Lastly, we find that hackers rely on two social engineering tricks in equal measure?luring users with free products and appealing to users' social curiosity?to enable socware cascades. Our findings present several promising avenues towards reducing socware on Facebook, but also highlight associated challenges.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {619–630},
numpages = {12},
keywords = {facebook, social spam, online social networks},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488444,
author = {Hurley, Ryan and Prusty, Swagatika and Soroush, Hamed and Walls, Robert J. and Albrecht, Jeannie and Cecchet, Emmanuel and Levine, Brian Neil and Liberatore, Marc and Lynn, Brian and Wolak, Janis},
title = {Measurement and Analysis of Child Pornography Trafficking on P2P Networks},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488444},
doi = {10.1145/2488388.2488444},
abstract = {Peer-to-peer networks are the most popular mechanism for the criminal acquisition and distribution of child pornography (CP). In this paper, we examine observations of peers sharing known CP on the eMule and Gnutella networks, which were collected by law enforcement using forensic tools that we developed. We characterize a year's worth of network activity and evaluate different strategies for prioritizing investigators' limited resources. The highest impact research in criminal forensics works within, and is evaluated under, the constraints and goals of investigations. We follow that principle, rather than presenting a set of isolated, exploratory characterizations of users.First, we focus on strategies for reducing the number of CP files available on the network by removing a minimal number of peers. We present a metric for peer removal that is more effective than simply selecting peers with the largest libraries or the most days online. Second, we characterize six aggressive peer subgroups, including: peers using Tor, peers that bridge multiple p2p networks, and the top 10% of peers contributing to file availability. We find that these subgroups are more active in their trafficking, having more known CP and more uptime, than the average peer. Finally, while in theory Tor presents a challenge to investigators, we observe that in practice offenders use Tor inconsistently. Over 90% of regular Tor users send traffic from a non-Tor IP at least once after first using Tor.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {631–642},
numpages = {12},
keywords = {digital forensics, forensic triage},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488445,
author = {Jamali, Mohsen and Lakshmanan, Laks},
title = {HeteroMF: Recommendation in Heterogeneous Information Networks Using Context Dependent Factor Models},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488445},
doi = {10.1145/2488388.2488445},
abstract = {With the growing amount of information available online, recommender systems are starting to provide a viable alternative and complement to search engines, in helping users to find objects of interest. Methods based on Matrix Factorization (MF) models are the state-of-the-art in recommender systems. The input to MF is user feedback, in the form of a rating matrix. However, users can be engaged in interactions with multiple types of entities across different contexts, leading to multiple rating matrices. In other words, users can have interactions in a heterogeneous information network. Generally, in a heterogeneous network, entities from any two entity types can have interactions with a weight (rating) indicating the level of endorsement. Collective Matrix Factorization (CMF) has been proposed to address the recommendation problem in heterogeneous networks. However, a main issue with CMF is that entities share the same latent factor across different contexts. This is particularly problematic in two cases: Latent factors for entities that are cold-start in a context will be learnt mainly based on the data from other contexts where these entities are not cold-start, and therefore the factors are not properly learned for the cold-start context. Also, if a context has more data compared to another context, then the dominant context will dominate the learning process for the latent factors for entities shared in these two contexts. In this paper, we propose a context-dependent matrix factorization model, HeteroMF, that considers a general latent factor for entities of every entity type and context-dependent latent factors for every context in which the entities are involved. We learn a general latent factor for every entity and transfer matrices for every context to convert the general latent factors into a context-dependent latent factor. Experiments on two real life datasets from Epinions and Flixster demonstrate that HeteroMF substantially outperforms CMF, particularly for cold-start entities and for contexts where interactions in one contexts are dominated by other contexts.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {643–654},
numpages = {12},
keywords = {matrix factorization, recommendation, cold start, multi-context},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488446,
author = {Jin, Xiaoran and Sloan, Marc and Wang, Jun},
title = {Interactive Exploratory Search for Multi Page Search Results},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488446},
doi = {10.1145/2488388.2488446},
abstract = {Modern information retrieval interfaces typically involve multiple pages of search results, and users who are recall minded or engaging in exploratory search using ad hoc queries are likely to access more than one page. Document rankings for such queries can be improved by allowing additional context to the query to be provided by the user herself using explicit ratings or implicit actions such as clickthroughs. Existing methods using this information usually involved detrimental UI changes that can lower user satisfaction. Instead, we propose a new feedback scheme that makes use of existing UIs and does not alter user's browsing behaviour; to maximise retrieval performance over multiple result pages, we propose a novel retrieval optimisation framework and show that the optimal ranking policy should choose a diverse, exploratory ranking to display on the first page. Then, a personalised re-ranking of the next pages can be generated based on the user's feedback from the first page. We show that document correlations used in result diversification have a significant impact on relevance feedback and its effectiveness over a search session. TREC evaluations demonstrate that our optimal rank strategy (including approximative Monte Carlo Sampling) can naturally optimise the trade-off between exploration and exploitation and maximise the overall user's satisfaction over time against a number of similar baselines.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {655–666},
numpages = {12},
keywords = {exploratory search, interactive ranking and retrieval, diversity},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488447,
author = {Kamath, Krishna Y. and Caverlee, James and Lee, Kyumin and Cheng, Zhiyuan},
title = {Spatio-Temporal Dynamics of Online Memes: A Study of Geo-Tagged Tweets},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488447},
doi = {10.1145/2488388.2488447},
abstract = {We conduct a study of the spatio-temporal dynamics of Twitter hashtags through a sample of 2 billion geo-tagged tweets. In our analysis, we (i) examine the impact of location, time, and distance on the adoption of hashtags, which is important for understanding meme diffusion and information propagation; (ii) examine the spatial propagation of hashtags through their focus, entropy, and spread; and (iii) present two methods that leverage the spatio-temporal propagation of hashtags to characterize locations. Based on this study, we find that although hashtags are a global phenomenon, the physical distance between locations is a strong constraint on the adoption of hashtags, both in terms of the hashtags shared between locations and in the timing of when these hashtags are adopted. We find both spatial and temporal locality as most hashtags spread over small geographical areas but at high speeds. We also find that hashtags are mostly a local phenomenon with long-tailed life spans. These (and other) findings have important implications for a variety of systems and applications, including targeted advertising, location-based services, social media search, and content delivery networks.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {667–678},
numpages = {12},
keywords = {spatial impact, social media, spatio-temporal analysis},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488448,
author = {Kim, Tiffany Hyun-Jin and Huang, Lin-Shung and Perrig, Adrian and Jackson, Collin and Gligor, Virgil},
title = {Accountable Key Infrastructure (AKI): A Proposal for a Public-Key Validation Infrastructure},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488448},
doi = {10.1145/2488388.2488448},
abstract = {Recent trends in public-key infrastructure research explore the tradeoff between decreased trust in Certificate Authorities (CAs), resilience against attacks, communication overhead (bandwidth and latency) for setting up an SSL/TLS connection, and availability with respect to verifiability of public key information. In this paper, we propose AKI as a new public-key validation infrastructure, to reduce the level of trust in CAs. AKI integrates an architecture for key revocation of all entities (e.g., CAs, domains) with an architecture for accountability of all infrastructure parties through checks-and-balances. AKI efficiently handles common certification operations, and gracefully handles catastrophic events such as domain key loss or compromise. We propose AKI to make progress towards a public-key validation infrastructure with key revocation that reduces trust in any single entity.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {679–690},
numpages = {12},
keywords = {tls, public-key infrastructure, accountability, public log servers, ssl, certificate validation},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488449,
author = {Kim, Younghoon and Park, Yoonjae and Shim, Kyuseok},
title = {DIGTOBI: A Recommendation System for Digg Articles Using Probabilistic Modeling},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488449},
doi = {10.1145/2488388.2488449},
abstract = {Digg is a social news website that lets people submit articles to share their favorite web pages (e.g. blog postings or news articles) and vote the articles posted by others. Digg service currently lists the articles in the front page by popularity without considering each user's preference to the topics in the articles. Helping users to find the most interesting Digg articles tailored to each user's own interests will be very useful, but it is not an easy task to classify the articles according to their topics in order to recommend the articles differently to each user.In this paper, we propose DIGTOBI, a personalized recommendation system for Digg articles using a novel probabilistic modeling. Our model considers the relevant articles with low Digg scores important as well. We show that our model can handle both warm-start and cold-start scenarios seamlessly through a single model. We next propose an EM algorithm to learn the parameters of our probabilistic model. Our performance study with Digg data confirms the effectiveness of DIGTOBI compared to the traditional recommendations algorithms.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {691–702},
numpages = {12},
keywords = {expectation-maximization, topic modeling, digg article recommendation, collaborative filtering, probabilistic latent semantic indexing},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488450,
author = {Krushevskaja, Darja and Sandler, Mark},
title = {Understanding Latency Variations of Black Box Services},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488450},
doi = {10.1145/2488388.2488450},
abstract = {Data centers run many services that impact millions of users daily. In reality, the latency of each service varies from one request to another. Existing tools allow to monitor services for performance glitches or service disruptions, but typically they do not help understanding the variations in latency.We propose a general framework for understanding performance of arbitrary black box services. We consider a stream of requests to a given service with their monitored attributes, as well as latencies of serving each request. We propose what we call the multi-dimensional f-measure, that helps for a given interval to identify the subset of monitored attributes that explains it. We design algorithms that use this measure not only for a fixed latency interval, but also to explain the entire range of latencies of the service by segmenting it into smaller intervals.We perform a detailed experimental study with synthetic data, as well as real data from a large search engine. Our experiments show that our methods automatically identify significant latency intervals together with request attributes that explain them, and are robust.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {703–714},
numpages = {12},
keywords = {performance analysis, algorithms, latency characterization},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488451,
author = {K\"{u}\c{c}\"{u}ktun\c{c}, Onur and Saule, Erik and Kaya, Kamer and \c{C}ataly\"{u}rek, \"{U}mit V.},
title = {Diversified Recommendation on Graphs: Pitfalls, Measures, and Algorithms},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488451},
doi = {10.1145/2488388.2488451},
abstract = {Result diversification has gained a lot of attention as a way to answer ambiguous queries and to tackle the redundancy problem in the results. In the last decade, diversification has been applied on or integrated into the process of PageRank- or eigenvector-based methods that run on various graphs, including social networks, collaboration networks in academia, web and product co-purchasing graphs. For these applications, the diversification problem is usually addressed as a bicriteria objective optimization problem of relevance and diversity. However, such an approach is questionable since a query-oblivious diversification algorithm that recommends most of its results without even considering the query may perform the best on these commonly used measures. In this paper, we show the deficiencies of popular evaluation techniques of diversification methods, and investigate multiple relevance and diversity measures to understand whether they have any correlations. Next, we propose a novel measure called expanded relevance which combines both relevance and diversity into a single function in order to measure the coverage of the relevant part of the graph. We also present a new greedy diversification algorithm called BestCoverage, which optimizes the expanded relevance of the result set with (1-1/e)-approximation. With a rigorous experimentation on graphs from various applications, we show that the proposed method is efficient and effective for many use cases.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {715–726},
numpages = {12},
keywords = {diversity, graph mining, relevance, result diversification},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488452,
author = {Kunegis, J\'{e}r\^{o}me and Preusse, Julia and Schwagereit, Felix},
title = {What is the Added Value of Negative Links in Online Social Networks?},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488452},
doi = {10.1145/2488388.2488452},
abstract = {We investigate the "negative link" feature of social networks that allows users to tag other users as foes or as distrusted in addition to the usual friend and trusted links. To answer the question whether negative links have an added value for an online social network, we investigate the machine learning problem of predicting the negative links of such a network using only the positive links as a basis, with the idea that if this problem can be solved with high accuracy, then the "negative link" feature is redundant. In doing so, we also present a general methodology for assessing the added value of any new link type in online social networks. Our evaluation is performed on two social networks that allow negative links: The technology news website Slashdot and the product review site Epinions. In experiments with these two datasets, we come to the conclusion that a combination of centrality-based and proximity-based link prediction functions can be used to predict the negative edges in the networks we analyse. We explain this result by an application of the models of preferential attachment and balance theory to our learning problem, and show that the "negative link" feature has a small but measurable added value for these social networks.},
booktitle = {Proceedings of the 22nd International Conference on World Wide Web},
pages = {727–736},
numpages = {10},
keywords = {enmity, link prediction, distrust, signed network, platform policies},
location = {Rio de Janeiro, Brazil},
series = {WWW '13}
}

@inproceedings{10.1145/2488388.2488453,
author = {Lin, Yu-Ru and Margolin, Drew and Keegan, Brian and Lazer, David},
title = {Voices of Victory: A Computational Focus Group Framework for Tracking Opinion Shift in Real Time},
year = {2013},
isbn = {9781450320351},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2488388.2488453},
doi = {10.1145/2488388.2488453},
abstract = {Social media have been employed to assess public opinions on events, markets, and policies. Most current work focuses on either developing aggregated measures or opinion extraction methods like sentiment analysis. These approaches suffer from unpredictable turnover in the participants and the information they react to, making it difficult to distinguish meaningful shifts from those that follow from known information. We propose a novel

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repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr