Social Medium
Social media analysis focuses on understanding and leveraging the vast amount of textual and multimedia data generated on online platforms to address societal challenges and scientific questions. Current research heavily utilizes large language models (LLMs) and transformer-based architectures, coupled with graph neural networks and other machine learning techniques, to detect harmful content (e.g., hate speech, suicide ideation, misinformation), analyze user behavior and sentiment, and predict societal trends. This field is significant for its potential to improve mental health interventions, mitigate the spread of harmful information, and enhance our understanding of social dynamics, impacting both the social sciences and the development of more responsible and ethical online platforms.
Papers
EVOLVE: Predicting User Evolution and Network Dynamics in Social Media Using Fine-Tuned GPT-like Model
Ismail Hossain, Md Jahangir Alam, Sai Puppala, Sajedul Talukder
Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media
Chen Chen, Mingwei Li, Fenghuan Li, Haopeng Chen, Yuankun Lin