Misinformation as a Misunderstood Challenge to Public Health

Southwell, Brian; Niederdeppe, Jeff; Cappella, Joseph N.; Gaysynsky, Anna; Kelley, Dannielle; Oh, April; Peterson, Emily; Chou, Wen‐Ying Sylvia

doi:10.1016/j.amepre.2019.03.009

Cited by 166 publications

(107 citation statements)

References 14 publications

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“…In addition, past meta-analyses offer little information with respect to the role played by social media in helping spread, as well as tag and debunk health-related falsehoods. Acknowledging that many individuals use social media as their primary source of health information (Chou et al, 2018) and that these platforms differ from other media in authorship, oversight, and algorithms (Southwell et al, 2019), assessing the efficacy of corrections on social media can go a long way in developing a proactive approach to fighting misinformation.…”

Section: The Current Meta-analysismentioning

confidence: 99%

Evaluating the Impact of Attempts to Correct Health Misinformation on Social Media: A Meta-Analysis

Walter¹,

Brooks²,

Saucier³

et al. 2020

Health Communication

233

114

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Social media poses a threat to public health by facilitating the spread of misinformation. At the same time, however, social media offers a promising avenue to stem the distribution of false claims-as evidenced by real-time corrections, crowdsourced fact-checking, and algorithmic tagging. Despite the growing attempts to correct misinformation on social media, there is still considerable ambiguity regarding the ability to effectively ameliorate the negative impact of false messages. To address this gap, the current study uses a meta-analysis to evaluate the relative impact of social media interventions designed to correct health-related misinformation (k = 24; N = 6,086). Additionally, the meta-analysis introduces theory-driven moderators that help delineate the effectiveness of social media interventions. The mean effect size of attempts to correct misinformation on social media was positive and significant (d = 0.40, 95% CI [0.25, 0.55], p =.0005) and a publication bias could not be excluded. Interventions were more effective in cases where participants were involved with the health topic, as well as when misinformation was distributed by news organizations (vs. peers) and debunked by experts (vs. non-experts). The findings of this meta-analysis can be used not only to depict the current state of the literature but also to prescribe specific recommendations to better address the proliferation of health misinformation on social media.

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Section: The Current Meta-analysismentioning

confidence: 99%

Evaluating the Impact of Attempts to Correct Health Misinformation on Social Media: A Meta-Analysis

Walter¹,

Brooks²,

Saucier³

et al. 2020

Health Communication

233

114

View full text Add to dashboard Cite

show abstract

“…Despite the risks of engaging with falsehoods circulating on the internet, misinformation that promotes problematic behaviors may require rigorous interventions (Chou, Oh, & Klein, 2018; Maibach, 2012; Southwell et al, 2019). The ability to correct circulating rumors is strongest when the scientific evidence against the misinformation is robust (Vraga & Bode, 2020).…”

Section: Challenge #3: Misinformationmentioning

confidence: 99%

Strategies for Effective Health Communication during the Coronavirus Pandemic and Future Emerging Infectious Disease Events

Vraga¹,

Jacobsen²

2020

World Med & Health Policy

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An emerging infectious disease event like the coronavirus (COVID‐19) pandemic demands careful communication of public health messages to diverse audiences. However, misinformation is easily generated and amplified when a rapidly evolving epidemiological situation is coupled with gaps in scientific knowledge about a novel pathogen. Groups involved in health policy research, advocacy, implementation, and enforcement must be aware of three key challenges as they develop and apply communication strategies during emergencies: (i) information overload, (ii) information uncertainty, and (iii) misinformation. These challenges can be countered by accurately communicating core messages to specific audiences, being transparent about the evidence base for recommendations and open about the likelihood that guidelines may change as scientific discoveries are made, and identifying which myths and conspiracy theories circulating on social media are so harmful that they must be directly countered. Data analytics can assist with the process of monitoring and evaluating the impact of organizational communications.

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“…Most misinformation-oriented discussions have been focused on venomous acts to taint the social media platforms with harmful and inaccurate information. False or fake news, misinterpretation of a drug protocol, or presentation of unrealistic claims have dramatic effects on public health (Southwell et al, 2019). The widespread misinformation, fake news, and rumors draw attention away from the exact information, real public health challenges, and healthcare professionals who are battling against the disease outbreak while protecting, as well as improving treatment modalities, and population health (Southwell et al, 2019).…”

Section: Public Health Disasters and Misinformationmentioning

confidence: 99%

“…False or fake news, misinterpretation of a drug protocol, or presentation of unrealistic claims have dramatic effects on public health (Southwell et al, 2019). The widespread misinformation, fake news, and rumors draw attention away from the exact information, real public health challenges, and healthcare professionals who are battling against the disease outbreak while protecting, as well as improving treatment modalities, and population health (Southwell et al, 2019). This misinformation and rumors act as a symbolic challenge to administrations combatting any public health crisis or disease outbreak.…”

Section: Public Health Disasters and Misinformationmentioning

confidence: 99%

Real-time credible online health information inquiring: a novel search engine misinformation notifier extension (SEMiNExt) during COVID-19-like disease outbreak

Shams

Apu

Rahman

et al. 2020

Preprint

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Public health-related misinformation spread rapidly in online networks, particularly, in social media during any disease outbreak. Misinformation of coronavirus disease 2019 (COVID-19) drug protocol or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities are utilizing several surveillance tools to detect, and slow down the rapid misinformation spread online, still millions of misinformation are found online. However, there is no currently available tool for receiving real-time misinformation notification during online health or COVID-19 related inquiries. Our proposed novel combinational approach, where we have integrated machine learning techniques with novel search engine misinformation notifier extension (SEMiNExt), helps to understand which news or information is from unreliable sources in real-time. The extension filters the search results and shows notification beforehand; it is a new and unexplored approach to prevent the spread of misinformation. To validate the user query, SEMiNExt transfers the data to a machine learning algorithm or classifier which predicts the authenticity of the search inquiry and sends a binary decision as either true or false. The results show that the supervised learning algorithm works best when 80% of the data set have been used for training purpose. Also, 10-fold cross-validation demonstrate a maximum accuracy and F1-score of 84.3% and 84.1% respectively for the Decision Tree classifier while the K-nearest-neighbor (KNN) algorithm shows the least performance. The SEMiNExt approach has introduced the possibility to improve online health communication system by showing misinformation notifications in real-time which enables safer web-based searching while inquiring on health-related issues.

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Misinformation as a Misunderstood Challenge to Public Health

Cited by 166 publications

References 14 publications

Evaluating the Impact of Attempts to Correct Health Misinformation on Social Media: A Meta-Analysis

Evaluating the Impact of Attempts to Correct Health Misinformation on Social Media: A Meta-Analysis

Strategies for Effective Health Communication during the Coronavirus Pandemic and Future Emerging Infectious Disease Events

Real-time credible online health information inquiring: a novel search engine misinformation notifier extension (SEMiNExt) during COVID-19-like disease outbreak

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