Mahathir Mohammad Bishal

Multilabel Aggressive Comments Detection from Social Media using Deep Learning Techniques

Mahathir Mohammad Bishal, Shawly Ahsan, Mohammed Moshiul Hoque

In recent years, researchers have attempted to identify and classify unwanted textual information (i.e., aggressive, abusive, offensive, hateful, and toxic) in online media due to its adverse effects on society. Several initiatives have been implemented to reduce the consumption and propagation of …

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COVIDHealth: A novel labeled dataset and machine learning-based web application for classifying COVID-19 discourses on Twitter

Mahathir Mohammad Bishal, Md. Rakibul Hassan Chowdory, Anik Das, Muhammad Ashad Kabir

The COVID-19 pandemic has sparked widespread health-related discussions on social media platforms like Twitter (now named 'X'). However, the lack of labeled Twitter data poses significant challenges for theme-based classification and tweet aggregation. To address this gap, we developed a machine learning-based …

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COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web Application for Classifying COVID-19 Discussions

Mahathir Mohammad Bishal, Md. Rakibul Hassan Chowdory, Anik Das, Muhammad Ashad Kabir

The COVID-19 pandemic has had adverse effects on both physical and mental health. During this pandemic, numerous studies have focused on gaining insights into health-related perspectives from social media. In this study, our primary objective is to develop a machine learning-based web …

COMBATANT@TamilNLP-ACL2022: Fine-grained Categorization of Abusive Comments using Logistic Regression

Alamgir Hossain, Mahathir Mohammad Bishal, Eftekhar Hossain, Omar Sharif et autres

With the widespread usage of social media and effortless internet access, millions of posts and comments are generated every minute.Unfortunately, with this substantial rise, the usage of abusive language has increased significantly in these mediums.This proliferation leads to many hazards such as …

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