Twitter Resource
Twitter data serves as a rich resource for studying various social phenomena, with research focusing on analyzing public opinion, detecting misinformation, and understanding online behavior. Current research employs a range of techniques, including transformer-based language models (like BERT and its variants), graph neural networks, and machine learning algorithms, to classify sentiment, identify bots and trolls, and detect hate speech or biased claims. These analyses provide valuable insights into public discourse, enabling improved understanding of social dynamics, the spread of misinformation, and the detection of harmful online activities, with implications for public health, political science, and social media regulation.
Papers
Inside the echo chamber: Linguistic underpinnings of misinformation on Twitter
Xinyu Wang, Jiayi Li, Sarah Rajtmajer
Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter
Cristian Rojas, Frank Algra-Maschio, Mark Andrejevic, Travis Coan, John Cook, Yuan-Fang Li