Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study

Abstract

Background COVID-19 vaccination is considered a critical prevention measure to help end the pandemic. Social media platforms such as Twitter have played an important role in the public discussion about COVID-19 vaccines. Objective The aim of this study was to investigate message-level drivers of the popularity and virality of tweets about COVID-19 vaccines using machine-based text-mining techniques. We further aimed to examine the topic communities of the most liked and most retweeted tweets using network analysis and visualization. Methods We collected US-based English-language public tweets about COVID-19 vaccines from January 1, 2020, to April 30, 2021 (N=501,531). Topic modeling and sentiment analysis were used to identify latent topics and valence, which together with autoextracted information about media presence, linguistic features, and account verification were used in regression models to predict likes and retweets. Among the 2500 most liked tweets and 2500 most retweeted tweets, network analysis and visualization were used to detect topic communities and present the relationship between the topics and the tweets. Results Topic modeling yielded 12 topics. The regression analyses showed that 8 topics positively predicted likes and 7 topics positively predicted retweets, among which the topic of vaccine development and people’s views and that of vaccine efficacy and rollout had relatively larger effects. Network analysis and visualization revealed that the 2500 most liked and most retweeted retweets clustered around the topics of vaccine access, vaccine efficacy and rollout, vaccine development and people’s views, and vaccination status. The overall valence of the tweets was positive. Positive valence increased likes, but valence did not affect retweets. Media (photo, video, gif) presence and account verification increased likes and retweets. Linguistic features had mixed effects on likes and retweets. Conclusions This study suggests the public interest in and demand for information about vaccine development and people’s views, and about vaccine efficacy and rollout. These topics, along with the use of media and verified accounts, have enhanced the popularity and virality of tweets. These topics could be addressed in vaccine campaigns to help the diffusion of content on Twitter.

COVID-19 Preventive Practices, Psychological Distress, and Reported Barriers to Healthcare Access during the Pandemic among Adult Community Members in Sub-Saharan Africa: A Phone Survey

“…pandemic progressed, the emergence of multiple COVID-19 vaccines, and the variations in COVID-19 caseloads in different parts of SSA. 43,[62][63][64][65] In addition, very small proportions of participants reported difficulty in accessing healthcare in urban and rural areas of Tanzania (Dodoma and Dar es Salaam), which is consistent with evidence that the stringency of prevention measures was very low in Tanzania at the time of the study and caseloads were decreasing. 47 We found several factors that were significantly related to self-reported difficulty in access to health services.…”

“…22,40,41 These findings also highlight the need for continuous efforts to disseminate accurate health information and education to the community to mitigate the pandemic. 42,43 In this survey, we found that the overall proportion of any psychological and mild psychological distress was lower than in the baseline survey (20% and 15.6%, respectively, in round 2, compared with 26% and 21% in round 1). However, moderate and severe psychological distress levels were comparable and remained consistently low.…”

Sentiment analysis of public sensitivity to COVID-19 vaccines on twitter by majority voting classifier-based machine learning

“…Pandeminin başlangıcı ve ortalarında COVID-19 virüsü üzerine pek çok duygu analizi çalışması mevcut olmakla birlikte [19][20][21][22] sosyal medyada COVID-19 aşılarına yönelik yapılan paylaşımların duygu analizi nispeten daha yeni bir çalışma alanı olarak karşımıza çıkmaktadır. Farklı bölgeler veya ülkeler için aşı hakkında içerik analizi [9,23,24], yapay zekâ ve makine öğrenmesi [18,25,26] veya sözlük tabanlı [5,15,17] duygu analizi çalışmaları COVID-19 aşısının dünya gündemine girmesi ile birlikte artmaktadır. Hussain vd.…”

Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets (Preprint)

“…Similarly, Yan et al [9] collected and analyzed Reddit comments about COVID-19 vaccines from three Canadian cities (from July 13, 2020, to June 14, 2021), and performed a comparison of the sentiment and main discussion topics among the three locations. Other recent works focused on analyzing sentiment and discussion topics in tweets about COVID-19 generated in other countries and in different time periods [20][21][22].…”