Identifying patterns to prevent the spread of misinformation during epidemics.

Nsoesie, Elaine O.; Oladeji, Olubusola

doi:10.37016/mr-2020-014

Cited by 15 publications

(13 citation statements)

References 16 publications

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“…Twitter users often fail to detect rumors (Wang & Zhuang, 2018). Misinformation for infectious diseases has focused primarily on issues related to transmission, prevention, treatment, and vaccination (Nsoesie & Oladeji, 2020). The false claims in misinformation can produce negative health outcomes and lead people to take harmful actions in search of a remedy, such as gargling bleach in attempt to eliminate a virus.…”

Section: Discussionmentioning

confidence: 99%

Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths

2020

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This research examines the content, timing, and spread of COVID-19 misinformation and subsequent debunking efforts for two COVID-19 myths. COVID-19 misinformation tweets included more non-specific authority references (e.g., “Taiwanese experts”, “a doctor friend”), while debunking tweets included more specific and verifiable authority references (e.g., the CDC, the World Health Organization, Snopes). Findings illustrate a delayed debunking response to COVID-19 misinformation, as it took seven days for debunking tweets to match the quantity of misinformation tweets. The use of non-specific authority references in tweets was associated with decreased tweet engagement, suggesting the importance of citing specific sources when refuting health misinformation.

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Section: Discussionmentioning

confidence: 99%

Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths

2020

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“…By harnessing a term/hashtag that was already in use on social media, the film's promoters had access to pre-existing and captive social networks through which to initiate the spread of the documentary. Online resources are emerging to investigate and track different conspiracy theories and viral misinformation related to COVID-19 (Chen et al, 2020;National Press Foundation, 2020;Nsoesie & Oladeji, 2020). Such tools support journalists as they respond in real-time to developing stories about viral misinformation, and public health researchers must adapt to evolving technologies.…”

Section: Plandemic As a Case Study For Social Media Misinformationmentioning

confidence: 99%

The Twitter origins and evolution of the COVID-19 “plandemic” conspiracy theory

2020

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Tweets about “plandemic” (e.g., #plandemic) – the notion that the COVID-19 pandemic was planned or fraudulent – helped to spread several distinct conspiracy theories related to COVID-19. But the term’s catchy nature attracted attention from anti-vaccine activist filmmakers who ultimately created Plandemic the 26-minute documentary. Plandemic falsely attacks NIAID Director Dr. Anthony Fauci, among others, and an eventual coronavirus vaccine. The film, which has since been widely discredited, appeared to at least temporarily shift Twitter communications to different topics and organizations, fueling the flow of conspiracy theories and misinformation itself with specific public figures to demonize.

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“…Another salient example is the COVID-19 pandemic. For instance, Nsoesie and Oladeji [ 17 ] investigated the impact of misinformation on public health during the COVID-19 pandemic. They found that COVID-19 misinformation prevented people from demonstrating effective health behaviors and weakened the public’s trust in the health care system.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study

Zhang¹,

Pian²,

Ma³

et al. 2021

JMIR Public Health Surveill

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Background The COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people’s health and governance systems. Objective This study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic. Methods We collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time. Results The daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: “conspiracy theories” (648/2745, 23.61%), “government response” (544/2745, 19.82%), “prevention action” (411/2745, 14.97%), “new cases” (365/2745, 13.30%), “transmission routes” (244/2745, 8.89%), “origin and nomenclature” (228/2745, 8.30%), “vaccines and medicines” (154/2745, 5.61%), and “symptoms and detection” (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations. Conclusions Our study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.

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Identifying patterns to prevent the spread of misinformation during epidemics.

Cited by 15 publications

References 16 publications

Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths

Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths

The Twitter origins and evolution of the COVID-19 “plandemic” conspiracy theory

Characterizing the COVID-19 Infodemic on Chinese Social Media: Exploratory Study

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