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
Understanding writing style in social media with a supervised contrastively pre-trained transformer
Javier Huertas-Tato, Alejandro Martin, David Camacho
MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts
Fardin Ahsan Sakib, Ahnaf Atef Choudhury, Ozlem Uzuner
LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics
Kaiqun Fu, Yangxiao Bai, Weiwei Zhang, Deepthi Kolady
Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms
Petter Törnberg, Diliara Valeeva, Justus Uitermark, Christopher Bail