Artikel

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

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.

Language
Englisch

Bibliographic citation
Journal: Journal of Business Economics ; ISSN: 1861-8928 ; Volume: 91 ; Year: 2021 ; Issue: 9 ; Pages: 1299-1329 ; Berlin, Heidelberg: Springer

Classification
Management
Computational Techniques; Simulation Modeling
Analysis of Collective Decision-Making: Other
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Network Formation and Analysis: Theory
Subject
Affordances
Agent-based simulation and modelling
Sense-making
Social influence
Social media
Social networks

Event
Geistige Schöpfung
(who)
Eismann, Kathrin
Event
Veröffentlichung
(who)
Springer
(where)
Berlin, Heidelberg
(when)
2021

DOI
doi:10.1007/s11573-020-01022-9
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Eismann, Kathrin
  • Springer

Time of origin

  • 2021

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