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Published on Authors of this article:. 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. 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. 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, 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 analyses of pro- and antivaccine discursive topics. 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 ourwe 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 show the urgency of developing clear problem statements for provaccine content to counteract the negative impact of antivaccine content on uptake rates. Vaccine-critical social media content has been suggested as a major obstacle to immunizing the public against vaccine-preventable diseases [ 1 - 4 ]. Simultaneously, the spread of antivaccine content has accelerated on social media [ 8 ], fostering groundless fears about immunization [ 9 ].

Exposure to antivaccine content on social media has been associated with delays in and refusal of vaccination [ 3 ]. While the development of tailored messages eg, text messages has increased immunization rates [ 10 ], public provaccine campaigns via social media have yielded limited success, as shown in several systematic reviews of interventions for various vaccines [ 411 Dancy at kerny latex personals 19 ]. Therefore, there is a need to compare pro- and antivaccine advocates in terms of the discursive topics they deploy on social media to engage and persuade audiences to accept or deny immunization.

Literature on digital media marketing suggests that the effectiveness of a campaign should be evaluated on a broader spectrum from engagement to persuasion because engaging audiences with content that competes against numerous other sources of content for their attention is a precursor to persuasion [ 2021 ]. This spectrum starts with engaging the audience with the content and concludes with persuading the audience to accept the claims included in the content.

As a way of fostering engagement, a greater diversity of discursive topics has been suggested [ 20 ]. Antivaccine advocates employ more diverse topics than their provaccine counterparts, and researchers have claimed that this in higher engagement [ 25 - 28 ]. However, the diversity of topics ie, the of topics discussed is not sufficient to harness public engagement [ 20 ]. Social media campaigns ought to ensure that the topics discussed are distinct from one another, thus attracting a wider range of individuals with diverse interests, and that discourse surrounding a topic is internally consistent and coherent so they make sense to the public [ 20 ].

We herein call the former intertopic distinctiveness and the latter intratopic consistency. Persuasion should follow engagement, which in this case is the effort by pro- and antivaccine advocates to encourage the public to accept or deny immunization. Framing vaccines in communications with individuals eg, parents has been suggested as a viable option for this purpose [ 2930 ].

Indeed, antivaccine advocates disproportionately emphasize safety concerns while downplaying the preventive benefits of vaccines. The overall objective of this study was to compare Dancy at kerny latex personals discursive topics pro- and antivaccine advocates deploy to influence the public to accept or deny immunization on the engagement-persuasion spectrum.

Our rationale for the first specific aim was that an automatic pro- or antivaccine classification is necessary for analyzing discursive topics on each side due to the sheer volume of vaccine-related content created and circulated on social media on a daily basis.

Our justification for the second specific aim was that we need an autonomous method that considers the numerous linguistic features included in pro- and antivaccine tweets and extracts topics from both sides without human bias. The achievement of these first two aims explains higher engagement among antivaccine advocates than their provaccine counterparts.

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In achieving these aims, we make several contributions to methodological advances. First, we collected a large coverage of both pro- and antivaccine social media posts that fairly represent both parties [ 32 ]. Second, we developed a machine learning ML -based automatic classifier of pro- and antivaccine posts and unsupervised clustering for extracting discursive topics. This set of ML algorithms will aid future researchers in assessing the effectiveness of public health campaigns on social media and hence facilitate the successful development of future interventions.

In so doing, we alleviated methodological challenges that have hindered an analysis of pro- and antivaccine discursive topics from a broader engagement-persuasion perspective.

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Antivaccine advocates on social media have shown more notable engagement patterns than their provaccine counterparts. On Instagram and Facebook, interaction tends to be higher with antivaccine content than with provaccine content [ 3334 ]. Antivaccine articles are shared more widely than provaccine articles [ 28 ]. Although the of provaccine tweets exceeds the of antivaccine tweets, the proportion of antivaccine users on Twitter is rising, having nearly doubled from 8.

Moreover, those who have been exposed to antivaccine content on Twitter and Facebook [ 3436 ] are more likely to disseminate similar antivaccine content. Parents exposed to antivaccine content on Facebook were 1. Antivaccine communities are more integrated with users who are undecided about vaccines compared to provaccine users, who remain on the periphery [ 26 ]. This higher user engagement has been attributed to a higher diversity of topics included in antivaccine rhetoric compared with provaccine content.

Strong themes have emerged among antivaccine communities, and they tend to cover a more expansive and generalizable range of content than provaccine communities [ 26 ]. Antivaccine communities also tend to share news reports and personal narratives among themselves, elevating the visibility and pertinence of select issues across communities on social media [ 428 ]. Memon et al [ 38 ] conducted a network and linguistic analysis of vaccine-related tweets and found that antivaccine communities use more specific, dramatized, and personalized linguistic features, have higher network density, and demonstrate higher echo-chamberness than do provaccine advocates.

Furini and Menegoni [ 39 ], Faasse et al [ 40 ], and Okuhara et al [ 41 ] defined the characteristics of topics used by pro- and antivaccine groups such as the tendency for antivaccine sites to focus on vaccine side effects and for provaccine sites to focus on vaccine primary effects. These seminal works, however, have not yet fully explicated the intertopic distinctiveness or intratopic consistency of discursive topics discussed by pro- and antivaccine advocates, even though Dancy at kerny latex personals factors are known to foster user engagement [ 2042 ].

Our suggested comparison between pro- and antivaccine content using the engagement-persuasion spectrum therefore helps explain both why and how antivaccine communities demonstrate higher engagement and affect uptake rates despite opposition from provaccine advocates. Recent studies on social media marketing posit that a variety of content should be created to actively engage customers in a dialogue with the speaker [ 20 ].

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This marketing perspective is relevant because pro- and antivaccine advocates compete to keep the audience engaged in their content with the ultimate goal of persuading the public for or against vaccines [ 26 ]. Although a diversity of topics in social media content is linked to increased user engagement, simply counting the of topics discussed is not sufficient [ 2042 ]. Instead, one ought to consider intertopic distinctiveness, which aids in serving a wider array of individuals with various interests [ 20 ]. For instance, if provaccine advocates discuss various issues only in the realm of contagious diseases eg, herd immunityindividuals who believe they have strong immunity eg, young people may not engage with such content.

Communicating the harms of the viral infection was not enough to encourage some people in their 20s and 30s to comply with the measures of state lockdowns or social distancing in the United States. It is therefore important to develop various distinct topics to attract individuals with different interests eg, herd immunity, fitness, and lifestyle. Next, intratopic consistency helps the audience make sense of the content, thereby facilitating the achievement of communication goals [ 20 ].

Especially in the uncontrolled space of social media, establishing consistency of messaging keeps the audience engaged [ 20 ]. Creating a coherent Dancy at kerny latex personals consistent image of a reference object in this case, vaccines by coordinating and connecting messages, arguments, and statements is an integral part of social media communication [ 20 ]. Accordingly, we assessed whether antivaccine topics indeed have higher intertopic distinctiveness and intratopic consistency than provaccine topics.

Our next step was to measure the persuasiveness of messages. Prior studies have suggested that message frames are a viable option in terms of counteracting ever decreasing immunization rates. For instance, McGlone et al [ 29 ] have studied the possibilities for provaccine framing by health care sources to communicate with parents through text messages. Antivaccine advocates emphasize injuries and conspiracies surrounding vaccines so as to convince viewers to consider vaccines unsafe, while provaccine advocates underscore the preventive benefits of vaccines and portray them as public health assets [ 46 ].

Entman [ 31 ] asserts that speakers frame an issue through 1 defining it, 2 interpreting its cause, 3 morally evaluating it, and 4 recommending a remedy to it. Parties that consistently use these four message frames have a greater influence on the majority Dancy at kerny latex personals receiving audiences [ 31 ], and this influence induces attitudinal and behavioral changes [ 24 ].

We adopted a multimethod approach to analyze discursive topics in large-scale vaccine debates on public social media sites. Our approach combined 1 large-scale balanced data collection from a public social media site ie, Twitter2 the development of a supervised classification algorithm for categorizing tweets, 3 the application of an unsupervised clustering algorithm for identifying prominent topics discussed on both sides, and 4 multistep qualitative content analysis for identifying the prominent discursive topics and how vaccines are framed in these topics.

Before and throughout our data collection, we identified, refined, and verified the keywords used to reach a large coverage of pro- and antivaccine tweets during our data collection period. Prior to embarking on the data collection, we studied academic and popular literature to identify relevant keywords and performed weekly tests by retrieving tweets using the search terms to ensure that they remained relevant.

In particular, from academic literature [ 25 ] and popular press articles about the vaccine debate from the Washington Postthe New York Time s, and Time magazine from January 1,to September 1,we initially identified a list of 81 keywords related to the vaccine debate on Twitter. Using these keywords, we then collected data every day in October and checked to determine how many tweets were retrieved on a weekly basis per keyword. From these weekly analyses, we eliminated 29 keywords for which the median weekly count of tweets retrieved was zero, because the absence of tweets retrieved by these keywords for an entire week indicated that these keywords were no longer relevant.

Finally, using the remaining 52 keywords, we collected tweets every day in November During our data collection in Novemberwe investigated whether any new topics or trending hashtags related to vaccines that had not been included in our set of keywords had emerged.

To do so, we referred to the list of the top 50 trending topics on Twitter, which has been used by prior researchers eg, Zubiaga et al [ 47 ] to identify popular topics that trigger wider conversations on Twitter. Following Zubiaga et al [ 47 ], we checked the top 50 trending topics and hashtags for each day in Novemberbut no new vaccine-related topics emerged. Because Twitter was the source of our data collection, the absence of emerging vaccine-related hashtags in the Twitter top 50 trending topics during our data collection period suggests that our data collection is comprehensive, up to date, and relevant.

A list of the 52 keywords is provided in Multimedia Appendix 1. Our use of 52 keywords could have led to repeated collection of the same tweets if we had not carefully tracked and eliminated them.

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The total of tweets collected was 39, 11, provaccine, antivaccine, and 20, neutral tweets. Next, we annotated tweets to construct our training set. Our initial annotation involved two members of the research team working simultaneously to ensure the correctness of annotations.

The two coauthors communicated throughout the annotation process to resolve any disagreements and ambiguities in the Dancy at kerny latex personals and to prevent any errors. This independent coder was thoroughly trained by a member of the author team. Upon completion of the training, the coder was given tweets to label as pro- or antivaccine. The set was an equal split between pro- and antivaccine tweets that had ly been annotated by the authors.

The interrater reliability, measured with the Cohen kappa agreement statistic [ 49 ], was 0. The annotated tweets were then used to train a classifier for labeling the vaccination stance of the tweets. For reproducibility purposes, the Jupyter notebook containing our Python code and the of its execution run can be obtained online [ 5051 ].

First, each of these annotated tweets was preprocessed to generate its feature vector representation. This function takes as input a tweet message and returns a vector of frequencies for each unigram, bigram, hashtag, or mention that appears in the tweet. After preprocessing, each tweet was represented by a feature vector of length 15, To do so, we iteratively chose nine of the 10 folds to be our training set while leaving the remaining fold out as test data. This process was repeated until each fold was used exactly once as the test data.

As the class distribution was potentially imbalanced, we also applied the oversampling technique on the training set to ensure that the induced model was not biased toward accurately predicting the larger class only. This was accomplished by resampling the training examples from the smaller classes ie, pro- and antivaccine until every class had an equal proportion in the training data. The logistic regression classifier was then trained on the balanced training data, and its induced model was then applied to the withheld test fold.

Logistic regression is a binary classifier for estimating the conditional probability that an input feature x belongs to class y using the following equation:. The coefficients were estimated during training using the maximum likelihood estimation approach. This approach can be described as follows. The logistic regression classifier was trained to minimize the following l1-regularized negative log-likelihood function:.

The l1-regularization penalty was used to prevent the model from overfitting the training data. Although it is possible to obtain better with more careful hyperparameter tuning, the default option was found to be sufficient to produce high accuracy. This is because the of training examples was sufficiently large to ensure that the test accuracy was quite stable. Furthermore, because there were three types of classification labels provaccine, antivaccine, and neutralthe classifier used the strategy of one versus all to train three binary models to predict each class.

Specifically, each binary model was trained to distinguish the tweets of one label eg, provaccine from the other two. In the prediction step, the classifier applied all three models to each given tweet and ased it to the class label with the highest aggregated confidence score. We evaluated the performance of the logistic regression classifier using stratified fold cross-validation. The classifier showed high overall classification accuracy of around The detailed classification for the three are shown in the confusion matrix in Table 1.

In addition, we report the precision, recall, and F-measure of the classifier for each tweet class in Table 2. The confusion matrix for the two is given in Table 3with an accuracy of around The precision, recall, and F-measure of the two are shown in Table 4. Note that if we had explicitly trained a logistic regression classifier to distinguish between the two provaccine or antivaccine vs neutral instead of simply aggregating the from Table 1we would have obtained a similar test accuracy of around Finally, we retrained the l1-regularized logistic regression classifier on the entire labeled tweets and applied them to the Twitter data we collected for November The final distribution of the classified tweets was as follows: provaccine, 11,; antivaccine, ; and neutral, 20, Next, we extracted the topical clusters of the pro- and antivaccine tweets that had been downloaded and classified as described above.

Specifically, we used the K-means algorithm in the Scikit-Learn Python package [ 52 ]. We chose K-means clustering because unlike other algorithms, it has high stability when employing a large amount of data with many dimensions [ 53 ]. Clusters derived from the K-means clustering algorithm contain common words mentioned at a similar frequency rate. Thus, each cluster shows a group of words that appear together frequently, comprising a topic of emerging tweets.

To determine the of clusters k in both Dancy at kerny latex personals sets, we measured their silhouette coefficients.

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Dancy at kerny latex personals