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Designing Better Credibility Indicators: Understanding How Emerging Adults Assess Source Credibility of Misinformation Identification and Labeling
Conference proceeding

Designing Better Credibility Indicators: Understanding How Emerging Adults Assess Source Credibility of Misinformation Identification and Labeling

Erica Shusas
Companion Publication of the 2024 ACM Designing Interactive Systems Conference, pp 41-44
01 Jul 2024
DOI: https://doi.org/10.1145/3656156.3665126

Abstract

Human-centered computing -- Human computer interaction (HCI) Human-centered computing -- Human computer interaction (HCI) -- Empirical studies in HCI
The proliferation of misinformation on social media platforms has impeded essential access to reliable information in the domains of health, elections, and politics. Recent advancements in generative AI and the propagation of AI-generated content (AIGC) have intensified the problem with the ease with which they can produce misinformation that is resilient to existing information assessment strategies. Content labels have become a preeminent means to indicate the credibility of social media posts. Emerging adults (ages 18-25) are ardent social media consumers, yet prior work has shown that credibility indicators are less impactful on this age group. My dissertation aims to better understand how emerging adults evaluate sources of misinformation identification and labels in order to offer insights into how to design better credibility assessment tools.

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