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
Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media
Yao Ge, Sudeshna Das, Karen O'Connor, Mohammed Ali Al-Garadi, Graciela Gonzalez-Hernandez, Abeed Sarker
Enhancing Suicide Risk Detection on Social Media through Semi-Supervised Deep Label Smoothing
Matthew Squires, Xiaohui Tao, Soman Elangovan, U Rajendra Acharya, Raj Gururajan, Haoran Xie, Xujuan Zhou
Similarity Guided Multimodal Fusion Transformer for Semantic Location Prediction in Social Media
Zhizhen Zhang, Ning Wang, Haojie Li, Zhihui Wang
A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media
Ayaz Mehmood, Muhammad Tayyab Zamir, Muhammad Asif Ayub, Nasir Ahmad, Kashif Ahmad
Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media
Gregorios Katsios, Ning Sa, Ankita Bhaumik, Tomek Strzalkowski