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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. ...Context 2
... 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. ...Context 3
... 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. ...Citations
... Social media communication pertaining to COVID-19 vaccines by governments and mainstream media tends to induce individuals' perception of disease risks to promote 12 protective vaccination behaviors . In addition, COVID-19 vaccine communications by personal social media accounts are often anti-vaccine, emphasizing the side effects of vaccines, alleging contamination of vaccinations, and highlighting potential value violation of vaccination (Klimiuk et al., 2021). ...
Social media use for risk communication during the COVID-19 pandemic has caused considerable concerns about an overabundance of information, particularly misinformation. However, how exposure to COVID-19 information on social media can lead to subsequent misinformation sharing during the pandemic has received little research attention. This study adopted the social amplification of risk framework to delineate how exposure to COVID-19 vaccine information on social media can be associated with individuals’ misinformation sharing through heuristic information processing. The role of social media trust was also examined. Results from an online survey (N = 1488) of Chinese Internet users revealed that exposure to COVID-19 vaccine information on social media was associated with misinformation sharing, mediated by both affect heuristics (i.e., negative affect toward the COVID-19 pandemic in general) and availability heuristics (i.e., perceived misinformation availability). Importantly, both high and low levels of trust in social media strengthened the mediating associations. While a low level of trust strengthened the association between exposure to COVID-19 vaccine information on social media and the affect heuristics, a high level of trust strengthened its association with the availability heuristics, both of which were associated with misinformation sharing. Our findings suggest that heuristic information processing is essential in amplifying the spread of misinformation after exposure to risk information on social media. It is also suggested that individuals should maintain a middle level of trust in social media, being open while critical of risk information on social media.
... In spite of sentiment analysis having become spread both in academic and corporate works [43,66,67], its evaluation is not free from risk: since NLP is a relatively young discipline which faces lots of challenging tasks, there are no current one-solves-all approaches for parsing human-produced syntax in a robust way. Besides, online chats might not be the most suited environment for unambiguous, error-free communication, not to mention the use of non-verbal means, such as non-plain-text characters (emojis, for instance) to express emotions and concepts which necessarily would go undetected by a not instructed software. ...
The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topic on which the public conversation is permeated by misleading claims, and thoughts tend to polarize, especially on online social networks. In this work, motivated by recent natural language processing techniques to systematically extract and quantify opinions from text messages, we present a differential framework for bivariate opinion formation dynamics that is coupled with a compartmental model for fake news dissemination. Thanks to a mean-field analysis we demonstrate that the resulting Fokker-Planck system permits to reproduce bimodal distributions of opinions as observed in polarization dynamics. The model is then applied to sentiment analysis data from social media platforms in Italy, in order to analyze the evolution of opinions about Covid-19 vaccination. We show through numerical simulations that the model is capable to describe correctly the formation of the bimodal opinion structure observed in the vaccine-hesitant dataset, which is witness of the known polarization effects that happen within closed online communities.
... Whereas ML and DL approaches was used in [191][192][193][194][195]. SA based on the BiLSTM DL approach was used in [191] to identify and assess vaccine-deniers' arguments against children vaccination. ...
... Whereas ML and DL approaches was used in [191][192][193][194][195]. SA based on the BiLSTM DL approach was used in [191] to identify and assess vaccine-deniers' arguments against children vaccination. The acquired data was manually categorized, which is a time-consuming operation. ...
... Google Cloud's Natural Language API was also used to analyze the sentiment of police agency Facebook pages in [197], They justified this use by pointing to their programming interface's simplicity and their industry-leading position in the disciplines of search and language processing. Similarly, in [191,192] posts and comments that did not contain any text, such as images or videos, were not considered. The study in [198] investigated the effect of a health-related issue on consumer responses on social media for a well-known and trusted business. ...
Analyzing and understanding the sentiments of social media documents on Twitter, Facebook, and Instagram has become a very important task at present. Analyzing the sentiment of these documents gives meaningful knowledge about the user opinions, which will help understand the overall view on these platforms. The problem of sentiment analysis (SA) can be regarded as a classification problem in which the text is classified as positive, negative, or neutral. This paper aims to give an intensive, but not exhaustive, review of the main concepts of SA and the state-of-the-art techniques; other aims are to make a comparative study of their performances, the main applications of SA as well as the limitations and the future directions for SA. Based on our analysis, researchers have utilized three main approaches for SA, namely lexicon/rules, machine learning (ML), and deep learning (DL). The performance of lexicon/rules-based models typically falls within the range of 55–85%. ML models, on the other hand, generally exhibit performance ranging from 55% to 90%, while DL models tend to achieve higher performance, ranging from 70% to 95%. These ranges are estimated and may be higher or lower depending on various factors, including the quality of the datasets, the chosen model architecture, the preprocessing techniques employed, as well as the quality and coverage of the lexicon utilized. Moreover, to further enhance models’ performance, researchers have delved into the implementation of hybrid models and optimization techniques which have demonstrated an ability to enhance the overall performance of SA models.
... For example, Jamison et al. 115 adapted Kata's typology to Twitter data using Latent Dirichlet Allocation to analyse 10,000 tweets, identifying the content attributes and observing that safety concerns and conspiracies were particularly prevalent in Twitter discussions. Likewise, Klimiuk et al. 116 also used Kata's content attributes to classify 4,042 Facebook posts and comments, finding that the most frequent content attributes were conspiracy theories/search for truth, misinformation and falsehoods, and safety and effectiveness. Broniatowski et al. 117 Other researchers have also developed typologies that are equally integrated into our taxonomy of 11 attitude roots and 62 themes. ...
The proliferation of anti-vaccination arguments is a threat to the success of many immunization programmes. Effective rebuttal of contrarian arguments requires an approach that goes beyond addressing flaws in the arguments, by also considering the attitude roots—that is, the underlying psychological attributes driving a person’s belief—of opposition to vaccines. Here, through a pre-registered systematic literature review of 152 scientific articles and thematic analysis of anti-vaccination arguments, we developed a hierarchical taxonomy that relates common arguments and themes to 11 attitude roots that explain why an individual might express opposition to vaccination. We further validated our taxonomy on coronavirus disease 2019 anti-vaccination misinformation, through a combination of human coding and machine learning using natural language processing algorithms. Overall, the taxonomy serves as a theoretical framework to link expressed opposition of vaccines to their underlying psychological processes. This enables future work to develop targeted rebuttals and other interventions that address the underlying motives of anti-vaccination arguments.
... Por su parte, un análisis de Google Trend realizado durante 2020 mostró que la seguridad (contenido de mercurio, relación con el autismo, posibles efectos secundarios) y las teorías conspirativas eran temas que se encontraban habitualmente en los mensajes antivacunación [Pullan & Dey, 2021]. Otro estudio analizó miles de comentarios antivacunas en Facebook y también encontró teorías conspirativas, contenidos relacionados con la seguridad y eficacia de las vacunas e incumplimiento de los derechos civiles, entre otros temas habituales [Klimiuk, Czoska, Biernacka & Balwicki, 2021]. Wawrzuta, Jaworski, Gotlib y Panczyk [2021] en una revisión sistemática mencionan que "los primeros estudios sobre el contenido compartido en sitios web antivacunación revelaron [. . . ...
En este artículo realizamos un relevamiento exhaustivo de las publicaciones de Médicos por la Verdad, uno de los principales grupos antivacunas de Latinoamérica, en la red social Twitter durante la pandemia de COVID-19. Clasificamos sus tipos de razonamiento y el contenido de sus mensajes y mostramos que las propuestas existentes de análisis de discursos anticientíficos no pueden aplicarse a este caso particular. Proponemos, en consecuencia, una nueva categorización y su aplicación focalizada en las herramientas disponibles para comunicadores de la ciencia, por un lado, y público no especializado, por el otro.
... Holding conspiracy beliefs is often associated with negative personal, social, and health-related consequences. For example, conspiracy beliefs are associated with reluctance to receive a COVID-19 vaccine and reduced adherence to public health regulations [4][5][6][7][8][9], extremist and violent behavior [10,11]. As much of this research shows only associations between conspiracy beliefs and negative outcome, further research is needed to draw any conclusions regarding causality. ...
Conspiracy beliefs have become a topic of increasing interest among behavioural researchers. While holding conspiracy beliefs has been associated with several detrimental social, personal, and health consequences, little research has been dedicated to systematically reviewing the methods that could reduce conspiracy beliefs. We conducted a systematic review to identify and assess interventions that have sought to counter conspiracy beliefs. Out of 25 studies (total N = 7179), we found that while the majority of interventions were ineffective in terms of changing conspiracy beliefs, several interventions were particularly effective. Interventions that fostered an analytical mindset or taught critical thinking skills were found to be the most effective in terms of changing conspiracy beliefs. Our findings are important as we develop future research to combat conspiracy beliefs.
... Kata (2010) and Jamison et al. (2020) identified five major categories of anti-vaccine misinformation, including safety and effectiveness, alternative medicine, civil liberties, conspiracy theories, and morality. In the context of the COVID-19 pandemic, big data analyses based on Twitter and Facebook revealed similar themes of vaccine misinformation (Skafle et al., 2022;Klimiuk et al., 2021). In the context of China, vaccine misinformation on Weibo was also manifested in similar forms (L. . ...
... In the context of China, vaccine misinformation on Weibo was also manifested in similar forms (L. . Such misinformation tended to use negative sentiment words to promote negative images of vaccines, such as emphasizing the side effects of vaccines, alleging contamination of vaccinations, and highlighting potential value violation of vaccination (Klimiuk et al., 2021). These practices are likely to induce negative outcome expectancies and negative attitudes toward vaccination. ...
... Those narratives tend to gather or even invent testimonies from communities of people affected by vaccines and vaccine-related practices, so as to promote public distrust of scientific evidence on vaccine safety and effectiveness (L. Klimiuk et al., 2021). In addition, online vaccine misinformation was often spread through an individual's social network. ...
Concerns have been raised about whether and how groups at high risk of COVID-19 are more likely affected by online vaccine misinformation during the pandemic. This study examined the associations between exposure to online vaccine misinformation and vaccination intention through vaccination perceptions and investigated the moderating role of individuals’ socioeconomic status. eHealth literacy was also investigated as a protective factor that mediated the effect of socioeconomic status. A survey of 1,700 Chinese netizens revealed that increased exposure to online COVID- 19 vaccine misinformation predicted lower vaccination intention, which was mediated by negative attitudes, lowered subjective norms, lowered perceived benefits, and higher perceived barriers toward vaccination. Socio-economic status (i.e., education, income, and residence), in general, did not guarantee individuals against the negative impacts of vaccine misinformation. eHealth literacy is critical in reducing susceptibility to
vaccine misinformation during the COVID-19 pandemic.
... Recently, experiments to identify COVID-19 vaccine skeptic content (Ng and Carley 2021) and a data set (Muric et al. 2021) were also published. For the Central-Eastern-Europe region, where Facebook is the most popular social network platform, similar results appeared using Facebook data (Klimiuk et al. 2021); however, Facebook has no public data access API, so its availability is strongly limited for research. As another alternative platform, research using data from Reddit also has appeared (Melton et al. 2021). ...
We investigate automatic methods to assess COVID vaccination views in Twitter content. Vaccine skepticism has been a controversial topic of long history that has become more important than ever with the COVID-19 pandemic. Our main goal is to demonstrate the importance of network effects in detecting vaccination skeptic content. Towards this end, we collected and manually labeled vaccination-related Twitter content in the first half of 2021. Our experiments confirm that the network carries information that can be exploited to improve the accuracy of classifying attitudes towards vaccination over content classification as baseline. We evaluate a variety of network embedding algorithms, which we combine with text embedding to obtain classifiers for vaccination skeptic content. In our experiments, by using Walklets, we improve the AUC of the best classifier with no network information by. We publicly release our labels, Tweet IDs and source codes on GitHub.
... In spite of sentiment analysis having become spread both in academic and corporate works [45,33,1], its evaluation is not free from risk: since NLP is a relatively young discipline which faces lots of challenging tasks, there are no current one-solves-all approaches for parsing humanproduced syntax in a robust way. Besides, online chats might not be the most suited environment for unambiguous, error-free communication, not to mention the use of non-verbal means, such as non-plain-text characters (emojis, for instance) to express emotions and concepts which necessarily would go undetected by a not instructed software. ...
The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topic on which the public conversation is permeated by misleading claims, and thoughts tend to polarize, especially on online social networks. In this work, motivated by recent natural language processing techniques to systematically extract and quantify opinions from text messages, we present a differential framework for bivariate opinion formation dynamics that is coupled with a compartmental model for fake news dissemination. Thanks to a mean-field analysis we demonstrate that the resulting Fokker-Planck system permits to reproduce bimodal distributions of opinions as observed in polarization dynamics. The model is then applied to sentiment analysis data from social media platforms in Italy, in order to analyze the evolution of opinions about Covid-19 vaccination. We show through numerical simulations that the model is capable to describe correctly the formation of the bimodal opinion structure observed in the vaccine-hesitant dataset, which is witness of the known polarization effects that happen within closed online communities.
... and "GreenMedInfo", whereas the quality of online alternative medicine information was found to be generally low [58,59]. Moreover, content promoting alternative medicine is often linked to anti-vaccine websites [60,61]. Likewise, the antivaccine sentiment was often associated with using alternative or non-western medicine from research analyzing 480 anti-vaccine web pages [60]. ...
... Similarly, our study that brings health-related digital media into misinformation literature showed consumption of AH media was linked to heightened misperception of vaccines, and this finding is the most consistent one across two countries in our research. It also confirms previous research that indicates a strong positive correlation between reliance on AH media and health-related misinformation such as vaccines and genetically modified foods in various contexts [11,17,61,83]. This again alerts us to pay closer attention to this type of media, especially during the global pandemic rife with misinformation as exposure to it might lead to serious consequences on health matters. ...
Being exposed to and believing in misinformation about COVID-19 vaccines is a challenge for vaccine acceptance. Yet, how countervailing factors such as news literacy could complicate "the information exposure-belief in vaccine misinformation-vaccination" path needs to be unpacked to understand the communication of scientific information about COVID-19. This study examines (1) the mediating role of belief in vaccine misinformation between COVID-19 information exposure and vaccination behavior and (2) the moderating role of news literacy behaviors. We examine these relationships by collecting data in two distinct societies: the United States and South Korea. We conducted online surveys in June and September 2021 respectively for each country (N = 1336 [the U.S.]; N = 550 [South Korea]). Our results showed a significant moderated mediation model, in which the association between digital media reliance and COVID-19 vaccination was mediated through vaccine misperceptions, and the relationship between digital media reliance and misinformed belief was further moderated by news literacy behavior. Unexpectedly, we found that individuals with stronger news literacy behavior were more susceptible to misinformation belief. This study contributes to the extant literature on the communication of COVID-19 science through probing into the mediating role of belief in vaccine-related misinformation and the moderating role of news literacy behavior in relation to COVID-19 information exposure and vaccination behaviors. It also reflects the concept of news literacy behavior and discusses how it could be further refined to exert its positive impact in correcting misinformation beliefs.
