Vaccine rhetoric on social media and COVID-19 vaccine uptake rates: A triangulation using self-reported vaccine acceptance.

The primary goal of this study is to examine the association between vaccine rhetoric on Twitter and the public's uptake rates of COVID-19 vaccines in the United States, compared to the extent of an association between self-reported vaccine acceptance and the CDC's uptake rates. We downloaded vaccine-related posts on Twitter in real-time daily for 13 months, from October 2021 to September 2022, collecting over half a billion tweets. A previously validated deep-learning algorithm was then applied to (1) filter out irrelevant tweets and (2) group the remaining relevant tweets into pro-, anti-, and neutral vaccine sentiments. Our results indicate that the tweet counts (combining all three sentiments) were significantly correlated with the uptake rates of all stages of COVID-19 shots (p < 0.01). The self-reported level of vaccine acceptance was not correlated with any of the stages of COVID-19 shots (p > 0.05) but with the daily new infection counts. These results suggest that although social media posts on vaccines may not represent the public's opinions, they are aligned with the public's behaviors of accepting vaccines, which is an essential step for developing interventions to increase the uptake rates. In contrast, self-reported vaccine acceptance represents the public's opinions, but these were not correlated with the behaviors of accepting vaccines. These outcomes provide empirical support for the validity of social media analytics for gauging the public's vaccination behaviors and understanding a nuanced perspective of the public's vaccine sentiment for health emergencies.

Do social media campaigns foster vaccination adherence? A systematic review of prior intervention-based campaigns on social media.

The COVID-19 pandemic has demonstrated the importance of large-scale campaigns to facilitate vaccination adherence. Social media presents unique opportunities to reach broader audiences and reduces the costs of conducting national or global campaigns aimed at achieving herd immunity. Nonetheless, few studies have reviewed the effectiveness of prior social media campaigns for vaccination adherence, and several prior studies have shown that social media campaigns do not increase uptake rates. Hence, our objective is to conduct a systematic review to examine the effectiveness of social media campaigns and to identify the reasons for the mixed results of prior studies. Our methodology began with a search of seven databases, which resulted in the identification of 92 interventions conducted over digital media. Out of these 92 studies, only 15 adopted social media campaigns for immunization. We analyzed these 15 studies, along with a coding scheme we developed based on reviews of both health interventions and social media campaigns. Multiple coders, who were knowledgeable about social media campaigns and healthcare, analyzed the 15 cases and obtained an acceptable level of inter-coder reliability (> .80). The results from our systematic review show that only a few social media campaigns have succeeded in enhancing vaccination adherence. In addition, few campaigns have utilized known critical success factors of social media to induce vaccination adherence. Based on these findings, we discuss a set of research questions that informatics scholars should consider when identifying opportunities for using social media to resolve one of the most resilient challenges in public health. Finally, we conclude by discussing how the insights drawn from our systematic reviews contribute to advancing theories, such as social influence and the health belief model, into the realm of social media-based health interventions.

Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media.

Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this disparity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, provaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics.

The mediating role of vaccine hesitancy between maternal engagement with anti- and pro-vaccine social media posts and adolescent HPV-vaccine uptake rates in the US: The perspective of loss aversion in emotion-laden decision circumstances.

While Human Papillomavirus (HPV) is a prominent cause of cervical cancer and mortality among underserved women, HPV vaccine completion rates remain stagnant (54%) among US adolescents. Our objective is to identify how adolescents' mothers' engagement with anti-vaccine versus pro-vaccine social media content is associated with their children's HPV vaccination rates via increased vaccine hesitancy. We employ the notion of loss aversion escalated in an emotion-laden circumstance in consumer behavior literature given that HPV vaccination decisions directly affect children's well-being. Based on this escalated loss aversion tendency for an emotion-laden decision, we explain why anti-vaccine content disproportionately increases mothers' overarching vaccine hesitancy, while pro-vaccine content does not decrease vaccine hesitancy. We conducted a population-based survey among 426 mothers of US adolescents aged 13-18. Our sample closely mimics the socioeconomic and demographic factors of the population group of mothers of adolescents in the US census. Our results show that anti-vaccine social media posts are associated with increases in mothers' overarching vaccine hesitancy and with decreases in their children's HPV vaccination rates, while pro-vaccine content has no significant association with either.

Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study.

BACKGROUND: Despite numerous counteracting efforts, antivaccine content linked to delays and refusals to vaccinate has grown persistently on social media, while only a few provaccine campaigns have succeeded in engaging with or persuading the public to accept immunization. Many prior studies have associated the diversity of topics discussed by antivaccine advocates with the public's higher engagement with such content. Nonetheless, a comprehensive comparison of discursive topics in pro- and antivaccine content in the engagement-persuasion spectrum remains unexplored. OBJECTIVE: We aimed to compare discursive topics chosen by pro- and antivaccine advocates in their attempts to influence the public to accept or reject immunization in the engagement-persuasion spectrum. Our overall objective was pursued through three specific aims as follows: (1) we classified vaccine-related tweets into provaccine, antivaccine, and neutral categories; (2) we extracted and visualized discursive topics from these tweets to explain disparities in engagement between pro- and antivaccine content; and (3) we identified how those topics frame vaccines using Entman's four framing dimensions. METHODS: We adopted a multimethod approach to analyze discursive topics in the vaccine debate on public social media sites. Our approach combined (1) large-scale balanced data collection from a public social media site (ie, 39,962 tweets from Twitter); (2) the development of a supervised classification algorithm for categorizing tweets into provaccine, antivaccine, and neutral groups; (3) the application of an unsupervised clustering algorithm for identifying prominent topics discussed on both sides; and (4) a multistep qualitative content analysis for identifying the prominent discursive topics and how vaccines are framed in these topics. In so doing, we alleviated methodological challenges that have hindered previous analyses of pro- and antivaccine discursive topics. RESULTS: Our results indicated that antivaccine topics have greater intertopic distinctiveness (ie, the degree to which discursive topics are distinct from one another) than their provaccine counterparts (t122=2.30, P=.02). In addition, while antivaccine advocates use all four message frames known to make narratives persuasive and influential, provaccine advocates have neglected having a clear problem statement. CONCLUSIONS: Based on our results, we attribute higher engagement among antivaccine advocates to the distinctiveness of the topics they discuss, and we ascribe the influence of the vaccine debate on uptake rates to the comprehensiveness of the message frames. These results show the urgency of developing clear problem statements for provaccine content to counteract the negative impact of antivaccine content on uptake rates.

Detecting Medical Misinformation on Social Media Using Multimodal Deep Learning.

In 2019, outbreaks of vaccine-preventable diseases reached the highest number in the US since 1992. Medical misinformation, such as antivaccine content propagating through social media, is associated with increases in vaccine delay and refusal. Our overall goal is to develop an automatic detector for antivaccine messages to counteract the negative impact that antivaccine messages have on the public health. Very few extant detection systems have considered multimodality of social media posts (images, texts, and hashtags), and instead focus on textual components, despite the rapid growth of photo-sharing applications (e.g., Instagram). As a result, existing systems are not sufficient for detecting antivaccine messages with heavy visual components (e.g., images) posted on these newer platforms. To solve this problem, we propose a deep learning network that leverages both visual and textual information. A new semantic- and task-level attention mechanism was created to help our model to focus on the essential contents of a post that signal antivaccine messages. The proposed model, which consists of three branches, can generate comprehensive fused features for predictions. Moreover, an ensemble method is proposed to further improve the final prediction accuracy. To evaluate the proposed model's performance, a real-world social media dataset that consists of more than 30,000 samples was collected from Instagram between January 2016 and October 2019. Our 30 experiment results demonstrate that the final network achieves above 97% testing accuracy and outperforms other relevant models, demonstrating that it can detect a large amount of antivaccine messages posted daily. The implementation code is available at https://github.com/wzhings/antivaccine_detection.