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

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: 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<.001). Moreover, there was a clear political divide in the perception of politicians by urban and rural users; these users communicated stronger negative sentiments about Republican and Democratic politicians, respectively (P<.001). Regarding misinformation and conspiracy theories, urban users exhibited stronger negative sentiments about the "covidiots" and "China virus" topics, while rural users exhibited stronger negative sentiments about the "Dr. Fauci" and "plandemic" topics. Finally, we observed that urban users' sentiments about the economy appeared to transition from negative to positive in late 2021, which was in line with the US economic recovery.

Conclusions: This study demonstrates there is a statistically significant difference in the sentiments of urban and rural Twitter users regarding a wide range of COVID-19-related topics. This suggests that social media can be relied upon to monitor public sentiment during pandemics in disparate types of regions. This may assist in the geographically targeted deployment of epidemic prevention and management efforts.

Keywords: COVID-19; Twitter; data; epidemic; machine learning; management; model; prevention; rural; sentiment analysis; social media; topic analysis; training; urban; vaccination; word embedding.

Figure 1

An illustration of the research pipeline. w2v: word2vec.

Figure 2

Number of tweets collected in US urban core and small town/rural zip codes.

Figure 3

A 2D representation of uniform manifold approximation and projection clustering results for 20 topics. Each point represents a distinct hashtag.

Figure 4

The monthly trend in volume (A) and relevance to COVID-19 (B) for selected topics and categories. The black line indicates the number of monthly new COVID-19 cases in the United States. EUA: emergency use authorization.

Figure 5

Overall normalized urban and rural sentiment toward COVID-19 and 20 selected topics. The category ID for each topic is shown to the left of the topic name. The error bar indicates SD 1 for the sentiment. The 3 additional topics at the bottom (separated by the dotted lines) are displayed to provide readers with some intuition into the degree of positivity (or negativity) represented by the sentiment score. The raw P values from the Welch t tests are shown in the right column; bold text indicates a statistically significant difference (P<.05/20) after Bonferroni correction.

Figure 6

Monthly urban and rural sentiment regarding COVID-19–related topics. For each month, depicted on the x-axis, the center of a dot represents the sentiment value of the topic, while the size of the dot reflects the ratio of the volume of the topic’s current month’s tweets to the sum of the topic’s tweets for all months. The trend lines correspond to a locally weighted linear regression for urban core and small town/rural.