Social Medium Data
Social media data analysis leverages the vast amount of user-generated content to understand societal trends, behaviors, and opinions. Current research focuses on developing and applying advanced machine learning models, including transformer networks and graph neural networks, to analyze this complex, multimodal data for tasks such as sentiment analysis, event detection, and bias identification. This field is significant for its potential to improve public health interventions (e.g., identifying mental health risks), inform policy decisions (e.g., understanding public opinion on energy policy), and enhance our understanding of social dynamics across diverse cultures and contexts.
Papers
STREAM: Social data and knowledge collective intelligence platform for TRaining Ethical AI Models
Yuwei Wang, Enmeng Lu, Zizhe Ruan, Yao Liang, Yi Zeng
Transcending the Attention Paradigm: Representation Learning from Geospatial Social Media Data
Nick DiSanto, Anthony Corso, Benjamin Sanders, Gavin Harding
Using Twitter Data to Determine Hurricane Category: An Experiment
Songhui Yue, Jyothsna Kondari, Aibek Musaev, Randy K. Smith, Songqing Yue
Investigating disaster response through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season
Zihui Ma, Lingyao Li, Libby Hemphill, Gregory B. Baecher, Yubai Yuan