Kawsar Ahmed


2024

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CUET_NLP_GoodFellows@DravidianLangTech EACL2024: A Transformer-Based Approach for Detecting Fake News in Dravidian Languages
Md Osama | Kawsar Ahmed | Hasan Mesbaul Ali Taher | Jawad Hossain | Shawly Ahsan | Mohammed Moshiul Hoque
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages

In this modern era, many people have been using Facebook and Twitter, leading to increased information sharing and communication. However, a considerable amount of information on these platforms is misleading or intentionally crafted to deceive users, which is often termed as fake news. A shared task on fake news detection in Malayalam organized by DravidianLangTech@EACL 2024 allowed us for addressing the challenge of distinguishing between original and fake news content in the Malayalam language. Our approach involves creating an intelligent framework to categorize text as either fake or original. We experimented with various machine learning models, including Logistic Regression, Decision Tree, Random Forest, Multinomial Naive Bayes, SVM, and SGD, and various deep learning models, including CNN, BiLSTM, and BiLSTM + Attention. We also explored Indic-BERT, MuRIL, XLM-R, and m-BERT for transformer-based approaches. Notably, our most successful model, m-BERT, achieved a macro F1 score of 0.85 and ranked 4th in the shared task. This research contributes to combating misinformation on social media news, offering an effective solution to classify content accurately.

2023

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Score_IsAll_You_Need at BLP-2023 Task 1: A Hierarchical Classification Approach to Detect Violence Inciting Text using Transformers
Kawsar Ahmed | Md Osama | Md. Sirajul Islam | Md Taosiful Islam | Avishek Das | Mohammed Moshiul Hoque
Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

Violence-inciting text detection has become critical due to its significance in social media monitoring, online security, and the prevention of violent content. Developing an automatic text classification model for identifying violence in languages with limited resources, like Bangla, poses significant challenges due to the scarcity of resources and complex morphological structures. This work presents a transformer-based method that can classify Bangla texts into three violence classes: direct, passive, and non-violence. We leveraged transformer models, including BanglaBERT, XLM-R, and m-BERT, to develop a hierarchical classification model for the downstream task. In the first step, the BanglaBERT is employed to identify the presence of violence in the text. In the next step, the model classifies stem texts that incite violence as either direct or passive. The developed system scored 72.37 and ranked 14th among the participants.