Examining Rural and Urban Sentiment Difference in COVID-19-Related Topics on Twitter: Word Embedding-Based Retrospective Study

• Published in: Journal of medical Internet research 2023-02, Vol.25 (1), p.e42985
• Main Authors: Liu, Yongtai, Yin, Zhijun, Ni, Congning, Yan, Chao, Wan, Zhiyu, Malin, Bradley
• Format: Article
• Language: English
• Subjects: Approximation; Attitude; Clustering; Communication; Conspiracy; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 vaccines; Deployment; Economic recovery; Humans; Immunization; Investigations; Keywords; Machine learning; Mass media; Misinformation; Multimedia; Original Paper; Pandemics; Politicians; Postal codes; Prevention; Public opinion; Retrospective Studies; Rural areas; Rural communities; SARS-CoV-2; Sentiment analysis; Social Media; Social networks; Tagging; Topics; United States; Urban areas
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/42985

By the end of 2022, more than 100 million people were infected with COVID-19 in the United States, and the cumulative death rate in rural areas (383.5/100,000) was much higher than in urban areas (280.1/100,000). As the pandemic spread, people used social media platforms to express their opinions and concerns about COVID-19-related topics. This study aimed to (1) identify the primary COVID-19-related topics in the contiguous United States communicated over Twitter and (2) compare the sentiments urban and rural users expressed about these topics. We collected tweets containing geolocation data from May 2020 to January 2022 in the contiguous United States. We relied on the tweets' geolocations to determine if their authors were in an urban or rural setting. We trained multiple word2vec models with several corpora of tweets based on geospatial and timing information. Using a word2vec model built on all tweets, we identified hashtags relevant to COVID-19 and performed hashtag clustering to obtain related topics. We then ran an inference analysis for urban and rural sentiments with respect to the topics based on the similarity between topic hashtags and opinion adjectives in the corresponding urban and rural word2vec models. Finally, we analyzed the temporal trend in sentiments using monthly word2vec models. We created a corpus of 407 million tweets, 350 million (86%) of which were posted by users in urban areas, while 18 million (4.4%) were posted by users in rural areas. There were 2666 hashtags related to COVID-19, which clustered into 20 topics. Rural users expressed stronger negative sentiments than urban users about COVID-19 prevention strategies and vaccination (P