Susceptibility to Unreliable Information Sources: Swift Adoption with
Minimal Exposure
- URL: http://arxiv.org/abs/2311.05724v1
- Date: Thu, 9 Nov 2023 20:16:06 GMT
- Title: Susceptibility to Unreliable Information Sources: Swift Adoption with
Minimal Exposure
- Authors: Jinyi Ye, Luca Luceri, Julie Jiang, Emilio Ferrara
- Abstract summary: Users tend to adopt low-credibility sources with fewer exposures than high-credibility sources.
The adoption of information sources often mirrors users' prior exposure to sources with comparable credibility levels.
- Score: 10.288282142373976
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Misinformation proliferation on social media platforms is a pervasive threat
to the integrity of online public discourse. Genuine users, susceptible to
others' influence, often unknowingly engage with, endorse, and re-share
questionable pieces of information, collectively amplifying the spread of
misinformation. In this study, we introduce an empirical framework to
investigate users' susceptibility to influence when exposed to unreliable and
reliable information sources. Leveraging two datasets on political and public
health discussions on Twitter, we analyze the impact of exposure on the
adoption of information sources, examining how the reliability of the source
modulates this relationship. Our findings provide evidence that increased
exposure augments the likelihood of adoption. Users tend to adopt
low-credibility sources with fewer exposures than high-credibility sources, a
trend that persists even among non-partisan users. Furthermore, the number of
exposures needed for adoption varies based on the source credibility, with
extreme ends of the spectrum (very high or low credibility) requiring fewer
exposures for adoption. Additionally, we reveal that the adoption of
information sources often mirrors users' prior exposure to sources with
comparable credibility levels. Our research offers critical insights for
mitigating the endorsement of misinformation by vulnerable users, offering a
framework to study the dynamics of content exposure and adoption on social
media platforms.
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