Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter




Faculty/Professorship: Information Systems and Social Networks  
Author(s): Eismann, Kathrin  
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2022
Pages: 1299–1329
Source/Other editions: Journal of Business Economics, 91 (2021), 9, S. 1299–1329 - ISSN: 1861-8928
is version of: 10.1007/s11573-020-01022-9
Year of first publication: 2021
Language(s): English
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-irb-550463
Abstract: 
Social media networks (SMN) such as Facebook and Twitter are infamous for facilitating the spread of potentially false rumors. Although it has been argued that SMN enable their users to identify and challenge false rumors through collective efforts to make sense of unverified information—a process typically referred to as self-correction—evidence suggests that users frequently fail to distinguish among rumors before they have been resolved. How users evaluate the veracity of a rumor can depend on the appraisals of others who participate in a conversation. Affordances such as the searchability of SMN, which enables users to learn about a rumor through dedicated search and query features rather than relying on interactions with their relational connections, might therefore affect the veracity judgments at which they arrive. This paper uses agent-based simulations to illustrate that searchability can hinder actors seeking to evaluate the trustworthiness of a rumor’s source and hence impede self-correction. The findings indicate that exchanges between related users can increase the likelihood that trustworthy agents transmit rumor messages, which can promote the propagation of useful information and corrective posts.
GND Keywords: Agent <Künstliche Intelligenz>; Computersimulation; Social Media; Soziales Netzwerk
Keywords: Affordances, Agent-based simulation and modelling, Sense-making, Social influence, Social media, Social networks
DDC Classification: 004 Computer science  
RVK Classification: ST 515   
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/55046
Release Date: 19. August 2022

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