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
Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse
S. Chapagain, Y. Zhao, T. K. Rohleen, S. M. Hamdi, S. F. Boubrahimi, R. E. Flinn, E. M. Lund, D. Klooster, J. R. Scheer, C. J. Cascalheira
Evaluating LLMs Capabilities Towards Understanding Social Dynamics
Anique Tahir, Lu Cheng, Manuel Sandoval, Yasin N. Silva, Deborah L. Hall, Huan Liu
A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases
Yunchong Liu, Xiaorui Shen, Yeyubei Zhang, Zhongyan Wang, Yexin Tian, Jianglai Dai, Yuchen Cao
User-Aware Multilingual Abusive Content Detection in Social Media
Mohammad Zia Ur Rehman, Somya Mehta, Kuldeep Singh, Kunal Kaushik, Nagendra Kumar
Making Social Platforms Accessible: Emotion-Aware Speech Generation with Integrated Text Analysis
Suparna De, Ionut Bostan, Nishanth Sastry
Enriching GNNs with Text Contextual Representations for Detecting Disinformation Campaigns on Social Media
Bruno Croso Cunha da Silva, Thomas Palmeira Ferraz, Roseli De Deus Lopes