Filipo Sharevski (DePaul University), Amy Devine (DePaul University), Emma Pieroni (DePaul University), Peter Jachim (DePaul University)

In this paper we investigate what textit{folk models of misinformation} exist on social media with a sample of 235 social media users. Work on social media misinformation does not investigate how ordinary users deal with it; rather, the focus is mostly on the anxiety, tensions, or divisions misinformation creates. Studying only the structural aspects also overlooks how misinformation is internalized by users on social media and thus is quick to prescribe "inoculation" strategies for the presumed lack of immunity to misinformation. How users grapple with social media content to develop "natural immunity" as a precursor to misinformation resilience, however, remains an open question. We have identified at least five textit{folk models} that conceptualize misinformation as either: textit{political (counter)argumentation}, textit{out-of-context narratives}, textit{inherently fallacious information}, textit{external propaganda}, or simply textit{entertainment}. We use the rich conceptualizations embodied in these folk models to uncover how social media users minimize adverse reactions to misinformation encounters in their everyday lives.

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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