Dataset Information

Conspiracy beliefs and vaccination intent for COVID-19 in an infodemic.

ABSTRACT:

Background

The massive, free and unrestricted exchange of information on the social media during the Covid-19 pandemic has set fertile grounds for fear, uncertainty and the rise of fake news related to the virus. This "viral" spread of fake news created an "infodemic" that threatened the compliance with public health guidelines and recommendations.

Objective

This study aims to describe the trust in social media platforms and the exposure to fake news about COVID-19 in Lebanon and to explore their association with vaccination intent.

Methods

In this cross-sectional study conducted in Lebanon during July-August, 2020, a random sample of 1052 participants selected from a mobile-phone database responded to an anonymous structured questionnaire after obtaining informed consent (response rate = 40%). The questionnaire was conducted by telephone and measured socio-demographics, sources and trust in sources of information and exposure to fake news, social media activity, perceived threat and vaccination intent.

Results

Results indicated that the majority of participants (82%) believed that COVID-19 is a threat and 52% had intention to vaccinate. Exposure to fake/ unverified news was high (19.7% were often and 63.8% were sometimes exposed, mainly to fake news shared through Watsapp and Facebook). Trust in certain information sources (WHO, MoPH and TV) increased while trust in others (Watsapp, Facebook) reduced vaccination intent against Covid-19. Believing in the man-made theory and the business control theory significantly reduced the likelihood of vaccination intent (Beta = 0.43; p = 0.01 and Beta = -0.29; p = 0.05) respectively.

Conclusion

In the context of the infodemic, understanding the role of exposure to fake news and of conspiracy believes in shaping healthy behavior is important for increasing vaccination intent and planning adequate response to tackle the Covid-19 pandemic.

SUBMITTER: Ghaddar A

PROVIDER: S-EPMC8754330 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts.ObjectiveThe aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic.MethodsWe began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time.ResultsAnalysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events.ConclusionsAlthough we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.