Summary of A Longitudinal Sentiment Analysis Of Sinophobia During Covid-19 Using Large Language Models, by Chen Wang and Rohitash Chandra
A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models
by Chen Wang, Rohitash Chandra
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework utilizes large language models (LLMs) for longitudinal sentiment analysis of Twitter data to detect and evaluate Sinophobic sentiments during the COVID-19 pandemic. The results show a significant correlation between the spikes in Sinophobic tweets, Sinophobic sentiments, and surges in COVID-19 cases, revealing that the evolution of the pandemic influenced public sentiment and the prevalence of Sinophobic discourse. The framework also highlights the predominant presence of negative sentiments, such as annoyance and denial, which underscores the impact of political narratives and misinformation shaping public opinion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses big language models to analyze Twitter data and understand how people felt about China during the COVID-19 pandemic. It finds that when more people got sick, there were more mean tweets about China, showing how the pandemic affected public opinion. The study also shows that people mostly felt annoyed or denied the truth, which was influenced by misinformation and political narratives. |
Keywords
» Artificial intelligence » Discourse
