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
The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse
Xiaobo Guo, Neil Potnis, Melody Yu, Nabeel Gillani, Soroush Vosoughi
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model
Jiahao Wang, Amer Shalaby
A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media
Jiaqing Yuan, Ruijie Xi, Munindar P. Singh
On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook
Ana-Maria Bucur, Andreea-Codrina Moldovan, Krutika Parvatikar, Marcos Zampieri, Ashiqur R. KhudaBukhsh, Liviu P. Dinu
On Large Uni- and Multi-modal Models for Unsupervised Classification of Social Media Images: Nature's Contribution to People as a case study
Rohaifa Khaldi, Domingo Alcaraz-Segura, Ignacio Sánchez-Herrera, Javier Martinez-Lopez, Carlos Javier Navarro, Siham Tabik
Depression detection in social media posts using transformer-based models and auxiliary features
Marios Kerasiotis, Loukas Ilias, Dimitris Askounis