Abstract
One of the challenges today is to face fake news (false information) in health due to its potential impact on people’s lives. This article contributes to a new application of social impact in social media (SISM) methodology. This study focuses on the social impact of the research to identify what type of health information is false and what type of information is evidence of the social impact shared in social media. The analysis of social media includes Reddit, Facebook, and Twitter. This analysis contributes to identifying how interactions in these forms of social media depend on the type of information shared. The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and finally, deliberation contexts promoted in social media overcome false information about health. These results contribute to advancing knowledge in overcoming fake health-related news shared in social media.
Highlights
Fake news has been defined as fabricated information that imitates news media content in form but not in organizational process or intent, which overlaps with other information disorders, such as misinformation—false or misleading information—and disinformation, which is false information that is deliberately disseminated to deceive people [1]
The main research questions are as follows: RQ1) How are social media messages focused on fake health information or misinformation?; RQ2) How are social media messages focused on health evidence with potential or real social impacts?; and RQ3) How do interactions based on health evidence with potential or real social impacts help overcome misinformation or fake health information? The results indicate that messages focused on fake health information are mostly aggressive, those based on evidence of social impact are respectful and transformative, and deliberation contexts promoted in social media overcome false health-related information
This article demonstrates that social impact in social media (SISM) is a replicable methodology that has been successfully applied in social media analytics in relation to health and fake news, contributing to the further exploration of the possibilities of this methodology
Summary
Fake news has been defined as fabricated information that imitates news media content in form but not in organizational process or intent, which overlaps with other information disorders, such as misinformation—false or misleading information—and disinformation, which is false information that is deliberately disseminated to deceive people [1]. The impact of fake news in social media is a major concern in public health, as it can reduce or increase the effectiveness of programs, campaigns and initiatives aimed at citizens’ health, awareness and well-being. The advancements in the methodologies related to social media analysis provide new insights to unveil how citizens share health information and the ways in which fake news influences public health. Constitutes a novel methodology in both social media analytics and the evaluation of the social impact of research [2]. This article applies the SISM methodology to the specific case of fake news in health to identify the type of interactions related to the information shared in social media. Public Health 2020, 17, 2430; doi:10.3390/ijerph17072430 www.mdpi.com/journal/ijerph
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