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
Detection of depression on social networks using transformers and ensembles
Ilija Tavchioski, Marko Robnik-Šikonja, Senja Pollak
Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
Wei Qin, Zetong Chen, Lei Wang, Yunshi Lan, Weijieying Ren, Richang Hong
"HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media
Lingyao Li, Lizhou Fan, Shubham Atreja, Libby Hemphill
A User-Driven Framework for Regulating and Auditing Social Media
Sarah H. Cen, Aleksander Madry, Devavrat Shah
"Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb
Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy, Raminta Daniulaityte, Amit Sheth
Emotion fusion for mental illness detection from social media: A survey
Tianlin Zhang, Kailai Yang, Shaoxiong Ji, Sophia Ananiadou
On the Effectiveness of Image Manipulation Detection in the Age of Social Media
Rosaura G. VidalMata, Priscila Saboia, Daniel Moreira, Grant Jensen, Jason Schlessman, Walter J. Scheirer