80 citations · 99 across the 5 of their papers we have counts for
5 papers
NarrationDep: Narratives on Social Media For Automatic Depression Detection
Hamad Zogan, Imran Razzak, Shoaib Jameel +1
Social media posts provide valuable insight into the narrative of users and their intentions, including providing an opportunity to automatically model whether a social media user…
IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification
Abdullah Alsuhaibani, Hamad Zogan, Imran Razzak +2
Language models such as Bidirectional Encoder Representations from Transformers (BERT) have been very effective in various Natural Language Processing (NLP) and text mining tasks i…
DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media
Hamad Zogan, Imran Razzak, Shoaib Jameel +1
Twitter is currently a popular online social media platform which allows users to share their user-generated content. This publicly-generated user data is also crucial to healthcar…
Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia
Jianlong Zhou, Hamad Zogan, Shuiqiao Yang +3
The recent COVID-19 pandemic has caused unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, st…
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Hamad Zogan, Imran Razzak, Xianzhi Wang +2
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction. However, most of the existing machine learning m…