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Frequency of content criteria categories among all analyzed comments (n = 4042).

Frequency of content criteria categories among all analyzed comments (n = 4042).

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Introduction: Vaccinations are referred to as one of the greatest achievements of modern medicine. However, their effectiveness is also constantly denied by certain groups in society. This results in an ongoing dispute that has been gradually moving online in the last few years due to the development of technology. Our study aimed to utilize social...

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... other data on commenting users were recorded. Each comment was categorized into one argument category (Table 1). The comments were classified based on Kata 10 , who divided anti-vaccination content into categories. ...
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... the study period, 18,685 comments were collected, 4,042 of which were manually determined and classified to the categories described in Table 1. Other comments, mostly consisting of brief expressions of emotional reactions including single emoticons/emojis and some off-topic material, were not included in the analysis of the anti-vaccine content. ...
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... other data on commenting users were recorded. Each comment was categorized into one argument category (Table 1). The comments were classified based on Kata 10 , who divided anti-vaccination content into categories. ...
Context 4
... the study period, 18,685 comments were collected, 4,042 of which were manually determined and classified to the categories described in Table 1. Other comments, mostly consisting of brief expressions of emotional reactions including single emoticons/emojis and some off-topic material, were not included in the analysis of the anti-vaccine content. ...

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... Only Twitter allows filtering data by geographical location. In addition, previous studies describing sentiment toward vaccination in Poland [2,42] have shown a high convergence of anti-vaccine topics with other countries [1,3,4,18]. Table 4 presents the sociodemographic data on Polish users of Facebook, Twitter, Instagram, and TikTok [43][44][45][46]. ...
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During the COVID-19 pandemic, social media content analysis allowed for tracking attitudes toward newly introduced vaccines. However, current evidence is limited to single social media platforms. Our objective was to compare arguments used by anti-vaxxers in the context of COVID-19 vaccines across Facebook, Twitter, Instagram, and TikTok. We obtained the data set of 53,671 comments regarding COVID-19 vaccination published between August 2021 and February 2022. After that, we established categories of anti-vaccine content, manually classified comments, and compared the frequency of occurrence of the categories between social media platforms. We found that anti-vaxxers on social media use 14 categories of arguments against COVID-19 vaccines. The frequency of these categories varies across different social media platforms. The anti-vaxxers’ activity on Facebook and Twitter is similar, focusing mainly on distrust of government and allegations regarding vaccination safety and effectiveness. Anti-vaxxers on TikTok mainly focus on personal freedom, while Instagram users encouraging vaccination often face criticism suggesting that vaccination is a private matter that should not be shared. Due to the differences in vaccine sentiment among users of different social media platforms, future research and educational campaigns should consider these distinctions, focusing more on the platforms popular among adolescents (i.e., Instagram and TikTok).
... A number of studies referred to attitudes and beliefs towards vaccinations, showing that misconceptions, hesitancy or an antivaccination approach were associated with poor performance [19]. This can be considered at the following two levels: general beliefs (positive or negative) include convictions about influenza, understanding of the safety and effectiveness of vaccines, unique theories about the purposes of vaccination, civil obligations and liberties or trust in scientific authorities [20]; specific outcome expectancies include individuals' perception of links between action and subsequent outcomes and the specific gains and losses resulting from vaccination [18]. ...
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The influenza vaccination rate remains unsatisfactorily low, especially in the healthy adult population. The positive deviant approach was used to identify key psychosocial factors explaining the intention of influenza vaccination in medics and compare them with those in non-medics. Methods: There were 709 participants, as follows: 301 medics and 408 non-medics. We conducted a cross-sectional study in which a multi-module self-administered questionnaire examining vaccination beliefs, risk perception, outcome expectations (gains or losses), facilitators' relevance, vaccination self-efficacy and vaccination intention was adopted. We also gathered information on access to vaccination, the strength of the vaccination habit and sociodemographic variables. Results: We used SEM and were able to explain 78% of the variance in intention in medics and 56% in non-medics. We identified both direct and indirect effects between the studied variables. In both groups, the intention was related to vaccination self-efficacy, stronger habits and previous season vaccination, but access to vaccines was significant only in non-medics. Conclusions: Applying the positive deviance approach and considering medics as positive deviants in vaccination performance extended the perspective on what factors to focus on in the non-medical population. Vaccination promotion shortly before the flu season should target non- or low-intenders and also intenders by the delivery of balanced information affecting key vaccination cognitions. General pro-vaccine beliefs, which may act as implicit attitudes, should be created in advance to build proper grounds for specific outcome expectations and facilitators' recognition. It should not be limited only to risk perception. Some level of evidence-based critical beliefs about vaccination can be beneficial.
... Understanding public opinion also matters in public health, as in the case of addressing perceptions of vaccines (Raghupathi et al. 2020;Klimiuk et al. 2021), and predicting signals of depression in self-expressed social media posts (Wang et al. 2013;Coppersmith et al. 2014;Reece et al. 2017;Stupinski et al. 2021). ...
Article
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We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur. In particular, we consider tweets with hashtags #imwithher and #crookedhillary, both relating to Hillary Clinton’s presidential bid in 2016. We then analyze the network properties in conjunction with the word scores by comparing with null models to separate the effects of the network structure and the score distribution. Neutral words are found to be dominant and most words, regardless of polarity, tend to co-occur with neutral words. We do not observe any score homophily among positive and negative words. However, when we perform network backboning, community detection results in word groupings with meaningful narratives, and the happiness scores of the words in each group correspond to its respective theme. Thus, although we observe no clear relationship between happiness scores and co-occurrence at the node or edge level, a community-centric approach can isolate themes of competing sentiments in a corpus.
... 2 Spreaders of this misinformation include certain governments, politicians, celebrities, and other sources. 16,26,29 Other social media that were documented as advancing vaccine-opposing messages include Facebook, [31][32][33][34][35] Instagram, [36][37][38] Pinterest, [39][40][41] and YouTube. [42][43][44][45][46] Use of specific platforms is associated with different levels of trust in vaccination and intentions to get vaccinated, 47 and the vaccine-related content on these platforms differs. ...
... [52][53][54] Conspiracy theories are narratives that assign a strong potency of causation to evil forces and that have epistemologies that diverge from scientific methods of knowledge. 55 Conspiracy theories provide a powerful negative framing of vaccination, 32 and believing conspiracy theories is inversely correlated with the intention to get vaccinated for COVID-19. 56 Individuals who are exposed to anti-vaccine conspiracy theories are less likely to intent to vaccinate their children, and the effects of such exposure are long lasting. ...
Article
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High uptake of vaccinations is essential in fighting infectious diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Social media play a crucial role in propagating misinformation about vaccination, including through conspiracy theories and can negatively impact trust in vaccination. Users typically engage with multiple social media platforms; however, little is known about the role and content of cross-platform use in spreading vaccination-related information. This study examined the content and dynamics of YouTube videos shared in vaccine-related tweets posted to COVID-19 conversations before the COVID-19 vaccine rollout. We screened approximately 144 million tweets posted to COVID-19 conversations and identified 930,539 unique tweets in English that discussed vaccinations posted between 1 February and 23 June 2020. We then identified links to 2,097 unique YouTube videos that were tweeted. Analysis of the video transcripts using Latent Dirichlet Allocation topic modeling and independent coders indicate the dominance of conspiracy theories. Following the World Health Organization's declaration of the COVID-19 outbreak as a public health emergency of international concern, anti-vaccination frames rapidly transitioned from claiming that vaccines cause autism to pandemic conspiracy theories, often featuring Bill Gates. Content analysis of the 20 most tweeted videos revealed that the majority (n = 15) opposed vaccination and included conspiracy theories. Their spread on Twitter was consistent with spamming and coordinated efforts. These findings show the role of cross-platform sharing of YouTube videos over Twitter as a strategy to propagate primarily anti-vaccination messages. Future policies and interventions should consider how to counteract misinformation spread via such cross-platform activities.
... Studies have also been conducted to study misinformation active on social media during public crises as the research object and to identify emotions of Internet users in relation to comments under the misinformation. It was found that both the gender of Internet users and the subject category of the content had an impact on the change in emotions [42]. Some studies conducted sentiment analysis on popular misinformation on social media based on content analysis methods and found that as the content, form, and linguistics of misinformation changed, so did the public's sentiment. ...
Article
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Social networks are filled with a large amount of misinformation, which often misleads the public to make wrong decisions, stimulates negative public emotions, and poses serious threats to public safety and social order. The spread of misinformation in social networks has also become a widespread concern among scholars. In the study, we took the misinformation spread on social media as the research object and compared it with true information to better understand the characteristics of the spread of misinformation in social networks. This study adopts a deep learning method to perform content analysis and emotion analysis on misinformation dataset and true information dataset and adopts an analytic network process to analyze the differences between misinformation and true information in terms of network diffusion characteristics. The research findings reveal that the spread of misinformation on social media is influenced by content features and different emotions and consequently produces different changes. The related research findings enrich the existing research and make a certain contribution to the governance of misinformation and the maintenance of network order.
... Mild, nonsevere AEs have usually been ignored by medical communities because they are common to all vaccines. Antivax movements have emphasized severe AEs, which have been widely discussed in social media [59] in the wider context of vaccine safety [60,61]. In the discourse on COVID-19 vaccines, the main issues were that they were developed quickly and they could compromise safety. ...
Article
Background: There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (β=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions: After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.
... Sentiment analysis has also been applied to financial markets, where the opinion of market participants plays an important role in future prices [6][7][8]. Understanding public opinion also matters in public health, as in the case of addressing perceptions of vaccines [9,10], and predicting signals of depression in self-expressed social media posts [11][12][13][14]. ...
Preprint
Full-text available
We explore the relationship between context and happiness scores in political tweets using word co-occurrence networks, where nodes in the network are the words, and the weight of an edge is the number of tweets in the corpus for which the two connected words co-occur. In particular, we consider tweets with hashtags #imwithher and #crookedhillary, both relating to Hillary Clinton's presidential bid in 2016. We then analyze the network properties in conjunction with the word scores by comparing with null models to separate the effects of the network structure and the score distribution. Neutral words are found to be dominant and most words, regardless of polarity, tend to co-occur with neutral words. We do not observe any score homophily among positive and negative words. However, when we perform network backboning, community detection results in word groupings with meaningful narratives, and the happiness scores of the words in each group correspond to its respective theme. Thus, although we observe no clear relationship between happiness scores and co-occurrence at the node or edge level, a community-centric approach can isolate themes of competing sentiments in a corpus.
... Mild, non-severe AEs have usually been ignored by medical communities because they are common to all vaccines. Antivax movements have emphasized severe AEs, which have been widely discussed in social media [45] in the wider context of vaccine safety [46,47]. In the discourse on COVID-19 vaccines, the main issues were that they were developed quickly, and they could compromise safety. ...
Preprint
Background: There is a limited amount of data on the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V) safety profile. Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Materials and Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multi-label classifications using the deep neural language model BERT DeepPavlov, which we pre-trained on a Russian language corpus and applied to the Telegram messages. The resulting AUC score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: The results of the retrospective analysis showed that females reported more AEs than males (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.13-fold, P<.001), and the number of AEs decreased with age (β = .05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) compared with mRNA ones (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase III clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=.94, P=.02) with those reported in the Argentinian post-marketing AE registry. Conclusion: After receiving the Sputnik V vaccination, Telegram users complained about pain (47%), fever (47%), fatigue (34%), and headache (25%). The results showed that the AE profile of Sputnik V was comparable with other COVID-19 vaccines. Examining the sentinel properties of participatory trials (which is subject to self-reporting biases) could still provide meaningful information about pharmaceutics, especially if only a limited amount of information on AEs is provided by producers.
... Currently, text mining on social media is being applied to analyze public opinion on or responses to the COVID-19 pandemic [10,11]. Data mining and text analysis have also been used to study the attitudes of vaccine deniers and their reasons for rejecting vaccines [12]. Korea commenced COVID-19 vaccinations on 26 February 2021. ...
Article
Full-text available
The COVID-19 pandemic has affected the entire world, resulting in a tremendous change to people’s lifestyles. We investigated the Korean public response to COVID-19 vaccines on social media from 23 February 2021 to 22 March 2021. We collected tweets related to COVID-19 vaccines using the Korean words for “coronavirus” and “vaccines” as keywords. A topic analysis was performed to interpret and classify the tweets, and a sentiment analysis was conducted to analyze public emotions displayed within the retrieved tweets. Out of a total of 13,414 tweets, 3509 were analyzed after preprocessing. Eight topics were extracted using the Latent Dirichlet Allocation model, and the most frequently tweeted topic was vaccine hesitation, consisting of fear, flu, safety of vaccination, time course, and degree of symptoms. The sentiment analysis revealed a similar ratio of positive and negative tweets immediately before and after the commencement of vaccinations, but negative tweets were prominent after the increase in the number of confirmed COVID-19 cases. The public’s anticipation, disappointment, and fear regarding vaccinations are considered to be reflected in the tweets. However, long-term trend analysis will be needed in the future.
... There is a need to understand better if some rapid COVID-19 vaccine policy and technical reviews have built or eroded public trust or vaccine confidence. It has been contended that India's government may have been overly hasty with lack of transparency in India's vaccine (Covaxin) licensing arrangements [32]. ...
Article
Full-text available
Israel, the UK, the USA, and some other wealthier countries lead in the implementation of COVID-19 vaccine mass vaccination programmes. Evidence from these countries indicates that their ethnic minorities could be as disproportionately disadvantaged in COVID-19 vaccines roll-out as they were affected by COVID-19-related serious illnesses. Their disadvantage is linked to their lower social status and fewer social goods compared with dominant population groups. Albeit limited by methodology, early studies attribute lower uptake of COVID-19 amongst ethnic minorities to the wider determinants of vaccine uptake, hesitancy or lack of vaccine confidence, including lower levels of trust and greater concerns about vaccine safety. Early sentinel studies are needed in all early adopter countries. One emerging theme among those of reproductive age in minority communities concerns a worry regarding COVID-19 vaccine’s potential adverse effect on fertility. Respected professional groups reassure this is not a credible rationale. Drug and vaccine regulators use understandable, cautious and conditional language in emergency licencing of new gene-based vaccines. Technical assessments on whether there is any potential genotoxicity or reproductive toxicity should be more emphatic. From a public health perspective, sentinel studies should identify such community concerns and act early to produce convincing explanations and evidence. Local public health workforces need to be diverse, multiskilled, and able to engage well with minorities and vulnerable groups. The local Directors of Public Health in the UK are based in each local government area and have a remit and opportunity to stimulate speedy action to increase vaccine uptake. During the rapid Pandemic Pace of the vaccines roll-out, extra efforts to minimise uptake variations are likely to achieve improvements in the next year or two. We expect variations will not disappear however, given that underlying inequalities persist in less inclusive social systems.