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
Risk prediction of pathological gambling on social media
Angelina Parfenova, Marianne Clausel
EmoScan: Automatic Screening of Depression Symptoms in Romanized Sinhala Tweets
Jayathi Hewapathirana, Deshan Sumanathilaka
FewUser: Few-Shot Social User Geolocation via Contrastive Learning
Menglin Li, Kwan Hui Lim
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
Seyedeh Baharan Khatami, Harsh Parikh, Haowei Chen, Sudeepa Roy, Babak Salimi
Correcting misinformation on social media with a large language model
Xinyi Zhou, Ashish Sharma, Amy X. Zhang, Tim Althoff
HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text
Ritesh Kumar, Ojaswee Bhalla, Madhu Vanthi, Shehlat Maknoon Wani, Siddharth Singh