The Effectiveness of Social Norms in Fighting Fake News on Social Media

Gimpel, Henner; Heger, Sebastian; Olenberger, Christian; Utz, Lena

doi:10.1080/07421222.2021.1870389

Cited by 80 publications

(45 citation statements)

References 61 publications

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“…Correcting disinformation has become more complex and difficult as social media platforms have grown in popularity, catalysing the quick and widespread spread of misinformation. Social media networks are highly afflicted by misinformation, and it is a challenge to block or flag (re)transmission due to a lack of professional gatekeeping [ 85 , 86 ]. This issue is compounded by the fact that health information seeking and scanning behaviours on social media networks increase when faced with a public health crisis [ 87 ], with COVID-19 being no exception [ 88 , 89 ].…”

Section: Discussionmentioning

confidence: 99%

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Abdelkader

Lokker

2022

BMC Complement Med Ther

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Background Coronavirus disease 2019 (COVID-19) is a novel infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite the paucity of evidence, various complementary, alternative and integrative medicines (CAIMs) have been being touted as both preventative and curative. We conducted sentiment and emotion analysis with the intent of understanding CAIM content related to COVID-19 being generated on Twitter across 9 months. Methods Tweets relating to CAIM and COVID-19 were extracted from the George Washington University Libraries Dataverse Coronavirus tweets dataset from March 03 to November 30, 2020. We trained and tested a machine learning classifier using a large, pre-labelled Twitter dataset, which was applied to predict the sentiment of each CAIM-related tweet, and we used a natural language processing package to identify the emotions based on the words contained in the tweets. Results Our dataset included 28 713 English-language Tweets. The number of CAIM-related tweets during the study period peaked in May 2020, then dropped off sharply over the subsequent three months; the fewest CAIM-related tweets were collected during August 2020 and remained low for the remainder of the collection period. Most tweets (n = 15 612, 54%) were classified as positive, 31% were neutral (n = 8803) and 15% were classified as negative (n = 4298). The most frequent emotions expressed across tweets were trust, followed by fear, while surprise and disgust were the least frequent. Though volume of tweets decreased over the 9 months of the study, the expressed sentiments and emotions remained constant. Conclusion The results of this sentiment analysis enabled us to establish key CAIMs being discussed at the intersection of COVID-19 across a 9-month period on Twitter. Overall, the majority of our subset of tweets were positive, as were the emotions associated with the words found within them. This may be interpreted as public support for CAIM, however, further qualitative investigation is warranted. Such future directions may be used to combat misinformation and improve public health strategies surrounding the use of social media information.

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Section: Discussionmentioning

confidence: 99%

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Abdelkader

Lokker

2022

BMC Complement Med Ther

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“…For example, Andı and Akesson (2021) found that warning people about the abundance of false information and telling them that "most responsible people think twice before sharing articles with their network" significantly reduced the proportion of people willing to share a false news article with others. Gimpel et al (2021) report that exposing people to injunctive (what behaviour most people approve or disapprove of) but not descriptive (what other people do in certain situations) social norms increased the likelihood of participants reporting fake news posts on social media as misinformation; a combined approach (with both injunctive and descriptive norms) had the most substantial effect.…”

Section: Social-norms Nudgesmentioning

confidence: 95%

Countering Misinformation: Evidence, Knowledge Gaps, and Implications of Current Interventions

Roozenbeek¹,

Suiter²,

Culloty³

2022

Preprint

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Developing effective interventions to counter misinformation is an urgent goal, but it also presents conceptual, empirical, and practical difficulties, compounded by the fact that misinformation research is in its infancy. This paper provides researchers and policymakers with an overview of which individual-level interventions are likely to have an influence on the spread of, susceptibility to, or impact of misinformation. We review the evidence for the effectiveness of four categories of interventions: boosting (psychological inoculation, critical thinking, and media and information literacy); nudging (accuracy primes and social norms nudges); debunking (fact-checking); and automated content labelling. In each area, we assess the empirical evidence, key gaps in knowledge, and practical considerations. We conclude with a series of recommendations for policymakers and tech companies to ensure a comprehensive approach to tackling misinformation.

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“…Keywords and phrases such as “stay safe at home” are connected with positive feelings, reflecting people's confidence in how the situation is being handled ( Ridhwan & Hargreaves, 2021 ). In contrast, bias, criticism, and fake news on the internet can mislead people and dampen their enthusiasm for overcoming the crisis ( Bunker, 2020 ; Flew, 2021 ; Ginpel, Heger, & Olenberger et al, 2021 ; Zhu, Ding, & Yu et al, 2021 ). Moreover, the pandemic affects people's views about privacy.…”

Section: Literature Reviewmentioning

confidence: 99%

Study on the mechanism of public attention to a major event: The outbreak of COVID-19 in China

Liu

2022

Sustainable Cities and Society

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The Effectiveness of Social Norms in Fighting Fake News on Social Media

Cited by 80 publications

References 61 publications

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data

Countering Misinformation: Evidence, Knowledge Gaps, and Implications of Current Interventions

Study on the mechanism of public attention to a major event: The outbreak of COVID-19 in China

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