7 papers
A Benchmark Suite of Reddit-Derived Datasets for Mental Health Detection
Khalid Hasan, Jamil Saquer
The growing availability of online support groups has opened up new windows to study mental health through natural language processing (NLP). However, it is hindered by a lack of h…
Multiclass Hate Speech Detection with RoBERTa-OTA: Integrating Transformer Attention and Graph Convolutional Networks
Mahmoud Abusaqer, Jamil Saquer
Multiclass hate speech detection across demographic categories remains computationally challenging due to implicit targeting strategies and linguistic variability in social media c…
Mental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Models
Khalid Hasan, Jamil Saquer, Yifan Zhang
Millions of people openly share mental health struggles on social media, providing rich data for early detection of conditions such as depression, bipolar disorder, etc. However, m…
Efficient Hate Speech Detection: Evaluating 38 Models from Traditional Methods to Transformers
Mahmoud Abusaqer, Jamil Saquer, Hazim Shatnawi
The proliferation of hate speech on social media necessitates automated detection systems that balance accuracy with computational efficiency. This study evaluates 38 model configu…
Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media
Khalid Hasan, Jamil Saquer, Mukulika Ghosh
The rising prevalence of mental health disorders necessitates the development of robust, automated tools for early detection and monitoring. Recent advances in Natural Language Pro…
Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Khalid Hasan, Jamil Saquer
Bipolar disorder is a chronic mental illness frequently underdiagnosed due to subtle early symptoms and social stigma. This paper explores the advanced natural language processing…