4 papers
A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases
Yunchong Liu, Xiaorui Shen, Yeyubei Zhang +4
Social media platforms like Twitter, Facebook, and Instagram have facilitated the spread of misinformation, necessitating automated detection systems. This systematic review evalua…
Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection
Yeyubei Zhang, Zhongyan Wang, Zhanyi Ding +5
Social media has become an important source for understanding mental health, providing researchers with a way to detect conditions like depression from user-generated posts. This t…
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media
Zhanyi Ding, Zhongyan Wang, Yeyubei Zhang +5
Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation.…
Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges
Yuchen Cao, Jianglai Dai, Zhongyan Wang +4
The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through use…