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
Investigating Chain-of-thought with ChatGPT for Stance Detection on Social Media
Bowen Zhang, Xianghua Fu, Daijun Ding, Hu Huang, Genan Dai, Nan Yin, Yangyang Li, Liwen Jing
Leveraging Social Interactions to Detect Misinformation on Social Media
Tommaso Fornaciari, Luca Luceri, Emilio Ferrara, Dirk Hovy
Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis
Brianna M White, Chad A Melton, Parya Zareie, Robert L Davis, Robert A Bednarczyk, Arash Shaban-Nejad
MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media
Ciprian-Octavian Truică, Elena-Simona Apostol, Radu-Cătălin Nicolescu, Panagiotis Karras
Exploring Social Media for Early Detection of Depression in COVID-19 Patients
Jiageng Wu, Xian Wu, Yining Hua, Shixu Lin, Yefeng Zheng, Jie Yang