---
license: cc-by-4.0
task_categories:
- text-classification
language:
- bn
tags:
- text
- classification
- crime
- event
- detection
pretty_name: crime
size_categories:
- 1K<n<10K
---
About Dataset
Bengali Crime Event Dataset – Law & Order, Governance & Social Issues (2025)

This dataset contains Bengali news headlines annotated for crime and event understanding across law & order, legal activity, governance, and social-issue contexts.

Each record represents a real-world headline paired with four aligned labels capturing:
  Event type
  Location
  Participants
  Action / outcome
  
The dataset is designed for NLP research, media analytics, and policy-oriented analysis in low-resource languages.

Note: The dataset reflects news reporting, not legal judgments or verified outcomes.

Data Source & Curation:
  a. Headlines are collected from publicly available Bengali news sources
  b. All labels are manually standardized into consistent label spaces
  c. Headlines: Bengali
  d. Labels: English (for broader usability and cross-lingual research)

Dataset Structure:
  Total records: 1K–10K headlines
  Granularity: One headline = one primary event

Fields per record:
  Field	Description
  Title	Bengali news headline (UTF-8)
  EventType	High-level category (Violence / Legal Activity / etc.)
  Location	City or region tag
  Participants, Actor, group
  Action, Procedural state or outcome

Key Features:
  1. Multi-head labels: Enables joint or hierarchical modeling
  2. Low-resource friendly: Bengali text with English labels
  3. Extendable geography: Can be linked with gazetteers

Potential Use Cases:
  a. Text Classification: Single- or multi-head prediction
  b. Information Extraction: Actor/action inference from headlines
  c. Trend Analysis: City-wise or actor-wise event patterns
  d. Policy & Media Studies: Aggregate reporting insights

Recommended Splits & Metrics:
  Split: Stratified 70/15/15 or 70/10/20

Metrics:
  1. Macro-F1 (primary)
  2. Micro-F1 / Accuracy (secondary)

Imbalance handling:
  1. Class weights
  2. Focal loss
  3. Balanced sampling

License:
  This dataset is released under CC BY 4.0.
  Free to use, share, and adapt for research and educational purposes.
  Please use responsibly and avoid individual-level attribution or predictive policing.

Citation:
  @inproceedings{hossain2025masknet,
    title={MaskNet: Enhancing Crime Event Detection with Feature Masking and Dynamic Attention},
    author={Hossain, M. M. and Hossain, M. S. and Chaki, S. and Rahman, M. S. and Ali, A. S.},
    booktitle={Proceedings of the 2nd International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM)},
    year={2025},
    publisher={IEEE}
  }

GitHub Repository:
  https://github.com/MIthun667/Crime-Event-Detections/tree/main