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
SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media
Aiqi Jiang, Arkaitz Zubiaga
Machine Learning enabled models for YouTube Ranking Mechanism and Views Prediction
Vandit Gupta, Akshit Diwan, Chaitanya Chadha, Ashish Khanna, Deepak Gupta
GREENER: Graph Neural Networks for News Media Profiling
Panayot Panayotov, Utsav Shukla, Husrev Taha Sencar, Mohamed Nabeel, Preslav Nakov
Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues
Canyu Chen, Haoran Wang, Matthew Shapiro, Yunyu Xiao, Fei Wang, Kai Shu