Department of Computer Science, Vanderbilt University, Nashville, TN, United States.
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
J Med Internet Res. 2023 Feb 15;25:e42985. 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<.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.
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.
截至 2022 年底，美国已有超过 1 亿人感染 COVID-19，农村地区的累计死亡率（383.5/10 万）远高于城市地区（280.1/10 万）。随着疫情的蔓延，人们开始利用社交媒体平台表达他们对 COVID-19 相关话题的看法和担忧。
本研究旨在：（1）确定美国大陆地区通过 Twitter 传播的主要 COVID-19 相关话题；（2）比较城乡用户对这些话题的情绪。
我们收集了 2020 年 5 月至 2022 年 1 月在美国大陆地区带有地理位置数据的推文。我们根据推文的地理位置来确定其作者所在的是城市还是农村地区。我们利用基于地理位置和时间信息的多个 tweets 语料库训练了多个 word2vec 模型。我们利用基于所有推文的 word2vec 模型，识别出与 COVID-19 相关的标签，并通过标签聚类获得相关话题。然后，我们根据城市和农村 word2vec 模型中话题标签和对应意见形容词之间的相似性，对城市和农村用户的情绪进行了推断分析。最后，我们利用月度 word2vec 模型分析了情绪的时间趋势。
我们创建了一个包含 4 亿条推文的语料库，其中 3.5 亿条（86%）是由城市地区的用户发布的，而 1800 万条（4.4%）是由农村地区的用户发布的。共有 2666 个与 COVID-19 相关的标签，聚类为 20 个话题。农村用户对 COVID-19 预防策略和疫苗接种的负面情绪比城市用户更强（P<.001）。此外，城乡用户对政客的看法存在明显的政治分歧；他们分别对共和党和民主党政客表达了更强的负面情绪（P<.001）。关于错误信息和阴谋论，城市用户对“covidiots”和“中国病毒”话题表现出更强的负面情绪，而农村用户对“Dr. Fauci”和“plandemic”话题表现出更强的负面情绪。最后，我们观察到，城市用户对经济的情绪似乎在 2021 年底从负面转向正面，这与美国经济复苏一致。
本研究表明，城乡 Twitter 用户在广泛的 COVID-19 相关话题上的情绪存在统计学上的显著差异。这表明社交媒体可以用来监测不同类型地区的公众情绪。这可能有助于在地理上有针对性地部署防疫和管理工作。
