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
Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles
Ivan Srba, Robert Moro, Matus Tomlein, Branislav Pecher, Jakub Simko, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, Adrian Gavornik, Maria Bielikova
Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision
Tunazzina Islam, Dan Goldwasser
MMGA: Multimodal Learning with Graph Alignment
Xuan Yang, Quanjin Tao, Xiao Feng, Donghong Cai, Xiang Ren, Yang Yang
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
Yizhou Zhang, Defu Cao, Yan Liu
Robust Candidate Generation for Entity Linking on Short Social Media Texts
Liam Hebert, Raheleh Makki, Shubhanshu Mishra, Hamidreza Saghir, Anusha Kamath, Yuval Merhav
DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities
Mohsinul Kabir, Tasnim Ahmed, Md. Bakhtiar Hasan, Md Tahmid Rahman Laskar, Tarun Kumar Joarder, Hasan Mahmud, Kamrul Hasan
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media
Momchil Hardalov, Anton Chernyavskiy, Ivan Koychev, Dmitry Ilvovsky, Preslav Nakov