When do people believe, check, and share health rumors on social media? Effects of evidence type, health literacy, and health knowledge

Xue, Haoning; Taylor, Laramie D.

doi:10.1177/13591053221125992

Cited by 9 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Wissen – oder vor allem fehlendes Wissen – hat einen Einfluss auf das Weiterleiten von Falschinformationen. Personen mit geringerem gesundheitlichen Vorwissen teilten Falschinformationen eher als Personen mit mehr Vorwissen [ 32 ]. Ein besseres Verständnis von wissenschaftlichen Abläufen führte eher dazu, dass weniger Falschinformationen geteilt wurden [ 33 ].…”

Section: Teilen Von Falsch- Und Desinformationen: Einflussfaktorenunclassified

Mis- and disinformation in social media: mitigating risks in digital health communication

Schüz,

Jones

2024

Bundesgesundheitsbl

View full text Add to dashboard Cite

ZusammenfassungInsbesondere im Kontext der COVID-19-Pandemie sind Falsch- und Desinformationen in sozialen Medien zu einer Herausforderung für effektive Public-health-Maßnahmen geworden. Hier betrachten wir Einflussfaktoren auf das Glauben und Teilen von Falschinformationen auf individueller, sozialer und situativ-kontextueller Ebene und diskutieren Interventionsmöglichkeiten.Auf individueller Ebene spielen Wissensdefizite, mangelnde Kompetenzen und emotionale Motivation eine Rolle. Menschen mit geringer Gesundheitskompetenz und bestimmten Überzeugungen sind anfälliger für Falschinformationen. Auf sozialer Ebene beeinflussen die Glaubwürdigkeit von Informationsquellen und soziale Normen das Teilen von Falschinformationen. Das Bedürfnis nach Status und Gruppenzugehörigkeit spielt ebenfalls eine Rolle. Auf kontextueller Ebene wirken Emotionen und die Wiederholung von Nachrichten auf das Glauben und Teilen von Falschinformationen.Interventionen auf individueller Ebene beinhalten Maßnahmen zur Verbesserung von Wissen und Kompetenzen. Auf sozialer Ebene können soziale Prozesse und soziale Normen angesprochen werden, um das Teilen von Falschinformationen zu reduzieren. Auf kontextueller Ebene wird die Regulierung von sozialen Netzwerken als wichtiger Ansatzpunkt betrachtet.Es wird darauf hingewiesen, dass soziale Ungleichheiten in der Exposition gegenüber Falschinformationen und im Umgang damit eine wichtige Rolle spielen. Es bleibt unklar, ob die Anfälligkeit für Falschinformationen eine generelle Tendenz oder kontextabhängig ist. Die Entwicklung von Interventionen muss daher vielfältige Einflussfaktoren berücksichtigen.

show abstract

Section: Teilen Von Falsch- Und Desinformationen: Einflussfaktorenunclassified

Mis- and disinformation in social media: mitigating risks in digital health communication

Schüz,

Jones

2024

Bundesgesundheitsbl

View full text Add to dashboard Cite

show abstract

“…Research on health rumors on social media in the United States and India are mainly categorized as epidemic-related health information, information epidemic, and anti-vaccine and vaccine hesitancy. However, research on the health information that is widely circulated on the social media is lacking [ 19 , 20 ]. Some studies have mainly focused on Twitter, YouTube, Facebook, and other social media platforms [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling

Li¹,

Wu²,

Lin³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Social networks have become one of the main channels for obtaining health information. However, they have also become a source of health-related misinformation, which seriously threatens the public’s physical and mental health. Governance of health-related misinformation can be implemented through topic identification of rumors on social networks. However, little attention has been paid to studying the types and routes of dissemination of health rumors on the internet, especially rumors regarding health-related information in Chinese social media. Objective This study aims to explore the types of health-related misinformation favored by WeChat public platform users and their prevalence trends and to analyze the modeling results of the text by using the Latent Dirichlet Allocation model. Methods We used a web crawler tool to capture health rumor–dispelling articles on WeChat rumor-dispelling public accounts. We collected information from health-debunking articles posted between January 1, 2016, and August 31, 2022. Following word segmentation of the collected text, a document topic generation model called Latent Dirichlet Allocation was used to identify and generalize the most common topics. The proportion distribution of the themes was calculated, and the negative impact of various health rumors in different periods was analyzed. Additionally, the prevalence of health rumors was analyzed by the number of health rumors generated at each time point. Results We collected 9366 rumor-refuting articles from January 1, 2016, to August 31, 2022, from WeChat official accounts. Through topic modeling, we divided the health rumors into 8 topics, that is, rumors on prevention and treatment of infectious diseases (1284/9366, 13.71%), disease therapy and its effects (1037/9366, 11.07%), food safety (1243/9366, 13.27%), cancer and its causes (946/9366, 10.10%), regimen and disease (1540/9366, 16.44%), transmission (914/9366, 9.76%), healthy diet (1068/9366, 11.40%), and nutrition and health (1334/9366, 14.24%). Furthermore, we summarized the 8 topics under 4 themes, that is, public health, disease, diet and health, and spread of rumors. Conclusions Our study shows that topic modeling can provide analysis and insights into health rumor governance. The rumor development trends showed that most rumors were on public health, disease, and diet and health problems. Governments still need to implement relevant and comprehensive rumor management strategies based on the rumors prevalent in their countries and formulate appropriate policies. Apart from regulating the content disseminated on social media platforms, the national quality of health education should also be improved. Governance of social networks should be clearly implemented, as these rapidly developed platforms come with privacy issues. Both disseminators and receivers of information should ensure a realistic attitude and disseminate health information correctly. In addition, we recommend that sentiment analysis–related studies be conducted to verify the impact of health rumor–related topics.

show abstract

How School Libraries Can Promote Health Literacy in Challenging Times

Merga

2023

Journal of Library Administration

View full text Add to dashboard Cite

When do people believe, check, and share health rumors on social media? Effects of evidence type, health literacy, and health knowledge

Cited by 9 publications

References 63 publications

Mis- and disinformation in social media: mitigating risks in digital health communication

Mis- and disinformation in social media: mitigating risks in digital health communication

Hot Topic Recognition of Health Rumors Based on Anti-Rumor Articles on the WeChat Official Account Platform: Topic Modeling

How School Libraries Can Promote Health Literacy in Challenging Times

Contact Info

Product

Resources

About