Theorizing Social Media Skepticism: How Two Distinct Types of Skepticism Impact Information Behaviors and Election Legitimacy

Social Data Dissertation Fellowship

Abstract

This project theorizes two distinct types of social media skepticism and tests their antecedents and ramifications in the context of the 2020 US presidential election. Despite the consensus on the importance of fostering skepticism for an informed citizenry, exactly what constitutes and underlies “healthy” skepticism toward social media misinformation is largely unknown. Existing research provides both hopeful and concerning findings on how social media skepticism relates to democratic outcomes. This project seeks to (a) establish conceptual and empirical differences between “accuracy motivated” and “directional motivated” skepticism toward social media misinformation and (b) identify the influence of discourses in news stories and social media utterances in fostering these two types of skepticism. Most importantly, this project seeks to (c) examine differential impacts of these two types of social media skepticism on outcomes crucial to democratic governance, including how citizens selectively choose ideological information, discern misinformation, and form views about election legitimacy. Using computational and panel survey approaches, this project tests these propositions with a combination of social media data, news media data, and public opinion data. While the definition of “healthy” skepticism will likely be the subject to a normative debate, the findings from this project will provide novel empirical evidence on the diverging consequences produced by two types of social media skepticism associated with how citizens consume information and interpret elections.

Research Team

Principal Investigator

Jianing Li

PhD Candidate and Knight Scholar of Communication and Civic Renewal, University of Wisconsin–Madison

  • Bio ▾

    Jianing "Janice" Li is a PhD candidate and a Knight Scholar of Communication and Civic Renewal in the School of Journalism and Mass Communication at University of Wisconsin–Madison. Li's research centers on misinformation, misperception, and fact-checking in new communication ecologies. Using computational, experimental, and social neuroscience methods, Li's work examines citizens’ knowledge in contested political and public health issues, psychological and contextual mechanisms contributing to misperceptions, dissemination of and discourse around misinformation in digital media environments, and effective corrective messages that facilitate belief and behavior change. Li's work has appeared in the Journal of Communication, Political Communication, Mass Communication and Society, and Social Media + Society, and in book chapter form at Routledge. Li also provides ongoing research support for COVID-19 Wisconsin Connect, a mobile and desktop app offering misinformation correction, social support, and helpful resources about Covid-19 to Wisconsinites.

Back to top