# Bangla Text Dataset and Exploratory Analysis for Online Harassment Detection

arXiv: https://arxiv.org/abs/2102.02478 · HTML: https://mlsanity.com/paper/2102.02478/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 4 Feb 2021.

## Built on by (most cited first, 7 of 7)

- [BanglaSarc: A Dataset for Sarcasm Detection](https://mlsanity.com/paper/2209.13461/) (2022)
- [Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions](https://mlsanity.com/paper/2401.07847/) (2024)
- [XAI in Computational Linguistics: Understanding Political Leanings in the Slovenian Parliament](https://mlsanity.com/paper/2305.04631/) (2023)
- [Detection of depression on social networks using transformers and ensembles](https://mlsanity.com/paper/2305.05325/) (2023)
- [Vulgar Remarks Detection in Chittagonian Dialect of Bangla](https://mlsanity.com/paper/2308.15448/) (2023)
- [Language-Independent Sentiment Labelling with Distant Supervision: A Case Study for English, Sepedi and Setswana](https://mlsanity.com/paper/2511.19818/) (2025)
- [BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION](https://mlsanity.com/paper/2610.13121/) (2026)

## Abstract (arXiv)

Being the seventh most spoken language in the world, the use of the Bangla language online has increased in recent times. Hence, it has become very important to analyze Bangla text data to maintain a safe and harassment-free online place. The data that has been made accessible in this article has been gathered and marked from the comments of people in public posts by celebrities, government officials, athletes on Facebook. The total amount of collected comments is 44001. The dataset is compiled with the aim of developing the ability of machines to differentiate whether a comment is a bully expression or not with the help of Natural Language Processing and to what extent it is improper if it is an inappropriate comment. The comments are labeled with different categories of harassment. Exploratory analysis from different perspectives is also included in this paper to have a detailed overview. Due to the scarcity of data collection of categorized Bengali language comments, this dataset can have a significant role for research in detecting bully words, identifying inappropriate comments, detecting different categories of Bengali bullies, etc. The dataset is publicly available at https://data.mendeley.com/datasets/9xjx8twk8p.