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The effectiveness of social norms in improving users’ reporting behavior in the fight against fake news

Title data

Gimpel, Henner ; Heger, Sebastian ; Olenberger, Christian ; Utz, Lena:
The effectiveness of social norms in improving users’ reporting behavior in the fight against fake news.
In: Journal of Management Information Systems. (2021) .

Project information

Project title:
Project's official titleProject's id
Projektgruppe WI Digital LifeNo information

Abstract in another language

Fake news poses a substantial threat to society with serious negative consequences. Therefore, we investigate how people can be encouraged to report fake news and support social media platform providers in their actions against misinformation. Based on social psychology, we hypothesize that social norms encourage social media users to report fake news. In two experiments, we present participants a news feed which contains multiple real and fake news stories while at the same time exposing them to injunctive and descriptive social norm messages. Injunctive social norms describe what behavior most people approve or disapprove. Descriptive social norms refer to what other people do in certain situations. The results reveal, among other things, that highlighting the socially desired behavior of reporting fake news using an injunctive social norm leads to higher reporting rates for fake news. In contrast, descriptive social norms do not have such an effect. Additionally, we observe that the combined application of injunctive and descriptive social norms results in the most substantial reporting behavior improvement. Thus, social norms are a promising socio-technical remedy against fake news.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: Fake news; Social norms; Online reporting behavior; Social media; Digital nudging
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 15 Dec 2020 08:43
Last Modified: 15 Dec 2020 08:43
URI: https://eref.uni-bayreuth.de/id/eprint/61096