Cuihua Shen

Salman Khawar, Yingdan Lu, Yilang Peng et al.

The rapid proliferation of visual content raises fundamental questions about how different visual formats and features shape perceived credibility. Drawing on processing fluency theory, this research examines how visuals shape credibility judgments. We focus on three popular formats-photos, infographics, and data visualizations-comparing them to text-only posts, and test how two visual features, aesthetic appeal and production quality, influence credibility through processing fluency as a mediating mechanism. Through a preregistered experiment with 1200 US participants, we found that visual posts are generally perceived as more credible than text-only posts but this credibility advantage only applies to photos and infographics, not to data visualizations. Aesthetic appeal increases perceived credibility, partially mediated by processing fluency, while production quality had no significant effect on credibility across formats. These findings differentiate visual formats, advance conceptualizations of visual features, and identify processing fluency as a key mechanism for theorizing credibility across multimodal contexts.

Sijia Qian, Cuihua Shen, Jingwen Zhang et al.

Cheapfakes, or real images presented misleadingly or in unrelated contexts, are an increasingly prominent form of visual misinformation. While media literacy interventions can enhance individuals' ability to detect such content, motivational barriers often hinder the adoption of image verification. This study examines whether incorporating different mechanisms and types of incentives into a digital media literacy intervention improves visual misinformation discernment and image verification behavior, both immediately and over time. We conducted a pre-registered two-wave between-subjects online experiment (N = 1,421) on a professionally designed social media platform. The study used a 2 (Incentive Type: symbolic vs. monetary) x 2 (Incentive Mechanism: task- vs. result-based) factorial design with additional control groups. Results show that task-based incentives, particularly monetary ones, were most effective at initiating image verification behaviors, namely reverse image search, and boosting short-term discernment, whereas result-based incentives were more effective in sustaining discernment accuracy. These findings suggest that both the mechanism and the type of incentives play a critical role in shaping the short- and long-term effectiveness of media literacy interventions, highlighting the value of multi-phased incentive strategies for combating visual misinformation in digital environments.

Sijia Qian, Cuihua Shen, Huiyi Wang et al.

Amid growing concern about information quality and credibility in digital media environments, researchers and educators still lack a concise, comprehensive yet psychometrically sound instrument for tracking the competencies that help people navigate this landscape. This article develops the Digital Media and Information Literacy Scale (DMILS), a robust and multidimensional measure that distinguishes domain (digital vs. information/news), competency type (knowledge vs. skill), and is measured through both subjective and objective items. Through two empirical studies with three nationally matched samples in the United States and Singapore (N = 1,498), we developed an 18-item self-report battery and 16-item objective knowledge questions, showing strong structural, convergent, and predictive validity, along with a short form (8 self-report and 8 objective items). By offering a parsimonious yet multidimensional yardstick, DMILS enables rigorous evaluation of media literacy interventions and supplies a common metric for cross-national research, critical for building an information ecosystem resilient to mis- and disinformation.

Cong Lin, Yifei Chen, Jiangyue Chen et al.

As visual misinformation becomes increasingly prevalent, platform algorithms act as intermediaries that curate information for users' verification practices. Yet, it remains unclear how algorithmic gatekeeping tools, such as reverse image search (RIS), shape users' information exposure during fact-checking. This study systematically audits Google RIS by reversely searching newly identified misleading images over a 15-day window and analyzing 34,486 collected top-ranked search results. We find that Google RIS returns a substantial volume of irrelevant information and repeated misinformation, whereas debunking content constitutes less than 30% of search results. Debunking content faces visibility challenges in rankings amid repeated misinformation and irrelevant information. Our findings also indicate an inverted U-shaped curve of RIS results page quality over time, likely due to search engine "data voids" when visual falsehoods first appear. These findings contribute to scholarship of visual misinformation verification, and extend algorithmic gatekeeping research to the visual domain.

Yilang Peng, Sijia Qian, Yingdan Lu et al.

In today's visually dominated social media landscape, predicting the perceived credibility of visual content and understanding what drives human judgment are crucial for countering misinformation. However, these tasks are challenging due to the diversity and richness of visual features. We introduce a Large Language Model (LLM)-informed feature discovery framework that leverages multimodal LLMs, such as GPT-4o, to evaluate content credibility and explain its reasoning. We extract and quantify interpretable features using targeted prompts and integrate them into machine learning models to improve credibility predictions. We tested this approach on 4,191 visual social media posts across eight topics in science, health, and politics, using credibility ratings from 5,355 crowdsourced workers. Our method outperformed zero-shot GPT-based predictions by 13 percent in R2, and revealed key features like information concreteness and image format. We discuss the implications for misinformation mitigation, visual credibility, and the role of LLMs in social science.

Kaiping Chen, Anfan Chen, Jingwen Zhang et al.

This paper studies conspiracy and debunking narratives about COVID-19 origination on a major Chinese social media platform, Weibo, from January to April 2020. Popular conspiracies about COVID-19 on Weibo, including that the virus is human-synthesized or a bioweapon, differ substantially from those in the US. They attribute more responsibility to the US than to China, especially following Sino-US confrontations. Compared to conspiracy posts, debunking posts are associated with lower user participation but higher mobilization. Debunking narratives can be more engaging when they come from women and influencers and cite scientists. Our findings suggest that conspiracy narratives can carry highly cultural and political orientations. Correction efforts should consider political motives and identify important stakeholders to reconstruct international dialogues toward intercultural understanding.