Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID-19

Zhou, Cheng; Xiu, Haoxin; Wang, Yuqiu; Yu, Xin‐Yao

doi:10.1016/j.ipm.2021.102554

Cited by 96 publications

(72 citation statements)

References 87 publications

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“…Low, moderate, and high information richness were coded as 1, 2, and 3, respectively. This measurement is consistent with previous arguments [ 15 , 44 , 92 ].…”

Section: Methodssupporting

confidence: 94%

“…The required sample size for NLP applied to health emergencies-related data is often unknown. Zhou et al [ 92 ] used 12,101 posts to explore the mechanism of the COVID-19-related misinformation on social media. A total of 1441 posts about the COVID-19 crisis were obtained by Chen et al [ 15 ] to explore how to promote citizen engagement.…”

Section: Methodsmentioning

confidence: 99%

“…NLP allows researchers to easily extract beneficial insights contained in textual datasets while avoiding burdensome computational work [ 48 ]. Third, some studies on health emergencies have utilized NLP to capture individuals' behaviors in the context of social media, for example, Zhou et al [ 92 ] explored the mechanism of COVID-19-related misinformation on social media based on NLP. Chen et al [ 15 ] analyzed the posts about the COVID-19 crisis in order to promote citizen engagement.…”

Section: Introductionmentioning

confidence: 99%

“…Contents can be posted in various formats on social media, such as text only, text + images, or text + videos. Individuals are more likely to be attracted to that vivid information and repost it [ 15 , 92 ]. Information richness can encourage greater involvement in information processing [ 88 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Zhou

Luo

et al. 2022

Decision Support Systems

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This paper investigates how information timeliness and richness affect public engagement using text data from China's largest social media platform during times of the COVID-19 pandemic. We utilize a similarity calculation method based on natural language processing (NLP) and text mining to evaluate three dimensions of information timeliness: retrospectiveness, immediateness, and prospectiveness. Public engagement is divided into breadth and depth. The empirical results show that information retrospectiveness is negatively associated with public engagement breadth but positively with depth. Both information immediateness and prospectiveness improved the breadth and depth of public engagement. Interestingly, information richness has a positive moderating effect on the relationships between information retrospectiveness, prospectiveness, and public engagement breadth but no significant effects on immediateness; meanwhile, it has a negative moderating effect on the relationship between retrospectiveness and depth but a positive effect on immediateness, prospectiveness. In the extension analysis, we constructed a supervised NLP model to identify and classify health emergency-related information (epidemic prevention and help-seeking) automatically. We find that public engagement differs in the two emergency-related information categories. The findings can promote a more responsive public health strategy that magnifies the transfer speed for critical information and mitigates the negative impacts of information uncertainty or false information.

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“…Low, moderate, and high information richness were coded as 1, 2, and 3, respectively. This measurement is consistent with previous arguments [ 15 , 44 , 92 ].…”

Section: Methodssupporting

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Zhou

Luo

et al. 2022

Decision Support Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ubiquity of social media has increasingly affected information dissemination and consumption during the COVID-19 pandemic 1 . With increasing social distancing and growing reliance on online communication, more and more people tend to use social platforms to seek information during the outbreak of COVID-19 2 . However, the complexity surrounding the pandemic not only comes from the virus itself, but also from the surge of the social and behavioural issues that the disease has brought about.…”

Section: Introductionmentioning

confidence: 99%

Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic

Jiang¹,

Song²,

Scarton³

et al. 2021

Preprint

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The spreading of COVID-19 misinformation over social media already draws the attention of many researchers. According to Google Scholar, about 26000 COVID-19 related misinformation studies have been published to date. Most of these studies focus on 1) detection and/or 2) analysing the characteristics of COVID-19 related misinformation. However, the study of the social behaviours related to misinformation is often neglected. In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e.g. comment or question to the misinformation). The dataset not only allows social behaviours analysis but is also suitable for both an evidence-based or non-evidence-based misinformation classification task. In addition, we introduce 'leave claim out' validation in our experiments and demonstrate that misinformation classification performance could be significantly different when applying to real-world unseen misinformation.

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Communicating shared situational awareness in times of chaos: Social media and theCOVID‐19 pandemic

Shahbazi

Bunker

Sorrell

2023

Asso for Info Science & Tech

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To effectively manage a crisis, most decisions made by governments, organizations, communities, and individuals are based on “shared situational awareness” (SSA) derived from multiple information sources. Developing SSA depends on the alignment of mental models, which “represent our shared version of truth and reality on which we can act.” Social media has facilitated public sensemaking during a crisis; however, it has also encouraged mental model dissonance, resulting in the digital destruction of mental models and undermining adequate SSA. The study is concerned with the challenges of creating SSA during the COVID‐19 pandemic in Australia. This paper documents a netnography of Australian public health agencies' Facebook communication, exploring the initial impact of COVID‐19 on SSA creation. Chaos theory is used as a theoretical lens to examine information perception, meaning, and assumptions relating to SSA from pre to post‐pandemic periods. Our study highlights how the initial COVID‐19 “butterfly effect” swamped the public health communication channel, leaving little space for other important health issues. This research contributes to information systems, information science, and communications by illustrating how the emergence of a crisis impacts social media communication, the creation of SSA, and what this means for social media adoption for crisis communication purposes.

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Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID-19

Cited by 96 publications

References 87 publications

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic

Communicating shared situational awareness in times of chaos: Social media and theCOVID‐19 pandemic

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