Understanding Public Opinion on Using Hydroxychloroquine for COVID-19 Treatment via Social Media

TL;DR

This study analyzes social media reactions and sentiment changes regarding hydroxychloroquine's proposed use for COVID-19, revealing fluctuating public opinion influenced by news and social feeds, with implications for understanding misinformation spread.

Contribution

It introduces a comprehensive analysis of social media data, linking tweet sentiments and search trends to public opinion dynamics on HCQ for COVID-19 treatment.

Findings

Public opinion supported HCQ early on but declined significantly by October.

Reaction patterns closely followed social and news media developments.

Google Cloud NLP outperformed other sentiment analysis tools, especially on sarcastic tweets.

Abstract

Hydroxychloroquine (HCQ) is used to prevent or treat malaria caused by mosquito bites. Recently, the drug has been suggested to treat COVID-19, but that has not been supported by scientific evidence. The information regarding the drug efficacy has flooded social networks, posting potential threats to the community by perverting their perceptions of the drug efficacy. This paper studies the reactions of social network users on the recommendation of using HCQ for COVID-19 treatment by analyzing the reaction patterns and sentiment of the tweets. We collected 164,016 tweets from February to December 2020 and used a text mining approach to identify social reaction patterns and opinion change over time. Our descriptive analysis identified an irregularity of the users' reaction patterns associated tightly with the social and news feeds on the development of HCQ and COVID-19 treatment. The study linked the tweets and Google search frequencies to reveal the viewpoints of local communities on the use of HCQ for COVID-19 treatment across different states. Further, our tweet sentiment analysis reveals that public opinion changed significantly over time regarding the recommendation of using HCQ for COVID-19 treatment. The data showed that high support in the early dates but it significantly declined in October. Finally, using the manual classification of 4,850 tweets by humans as our benchmark, our sentiment analysis showed that the Google Cloud Natural Language algorithm outperformed the Valence Aware Dictionary and sEntiment Reasoner in classifying tweets, especially in the sarcastic tweet group.