English Tweet

Research on English tweets focuses on automating the analysis of their content, primarily for tasks like topic classification, sentiment analysis, and identifying sensitive content such as hate speech or misinformation. This involves leveraging powerful pre-trained language models, particularly transformer architectures like BERT and its variants (e.g., RoBERTa, BERTweet), often employing techniques like fine-tuning or zero-shot prompt-based classification. These advancements enable scalable analysis of large volumes of tweet data, offering valuable insights into public opinion, health trends, and political discourse, with applications in digital epidemiology, social science research, and combating online harms. The field is actively exploring methods to improve accuracy and efficiency, particularly for low-resource languages and nuanced tasks requiring fine-grained analysis.