Associative Inference Can Increase People’s Susceptibility to Misinformation
DOI:
https://doi.org/10.1609/icwsm.v17i1.22166Keywords:
Credibility of online content, Psychological, personality-based and ethnographic studies of social media, Qualitative and quantitative studies of social media, Human computer interaction; social media tools; navigation and visualizationAbstract
Associative inference is an adaptive, constructive process of memory that allows people to link related information to make novel connections. We conducted three online human-subjects experiments investigating participants’ susceptibility to associatively inferred misinformation and its interaction with their cognitive ability and how news articles were presented. In each experiment, participants completed recognition and perceived accuracy rating tasks for the snippets of news articles in a tweet format across two phases. At Phase 1, participants viewed real news only. At Phase 2, participants viewed both real and fake news. Critically, we varied whether the fake news at Phase 2 was inferred from (i.e., associative inference), associated with (i.e., association only), or irrelevant to (i.e., control) the corresponding real news pairs at Phase 1. Both recognition and perceived accuracy results showed that participants in the associative inference condition were more susceptible to fake news than those in the other conditions. Furthermore, hashtags embedded within the tweets made the obtained effects evident only for the participants of higher cognitive ability. Our findings reveal that associative inference can be a basis for individuals’ susceptibility to misinformation, especially for those of higher cognitive ability. We conclude by discussing the implications of our results for understanding and mitigating misinformation on social media platforms.Downloads
Published
2023-06-02
How to Cite
Lee, S., Seo, H., Lee, D., & Xiong, A. (2023). Associative Inference Can Increase People’s Susceptibility to Misinformation. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 530-541. https://doi.org/10.1609/icwsm.v17i1.22166
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