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:
- Multi-head labels: Enables joint or hierarchical modeling
- Low-resource friendly: Bengali text with English labels
- 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:
- Macro-F1 (primary)
- Micro-F1 / Accuracy (secondary)
Imbalance handling:
- Class weights
- Focal loss
- 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