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
A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter
Kyra Yee, Alice Schoenauer Sebag, Olivia Redfield, Emily Sheng, Matthias Eck, Luca Belli
Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts
Asahi Ushio, Leonardo Neves, Vitor Silva, Francesco Barbieri, Jose Camacho-Collados