arXiv subjects

Cuihua Shen

Publications and source records attributed to Cuihua Shen.

12 recordsLinked to original sources

arXiv2026

Building Resilience to Misinformation: A Cross-National Development of the Digital Media and Information Literacy Scale (DMILS)

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.

cs.CY↗

arXiv2026

Gendered Pixels: Exploring Gender Differences in Computer-Mediated Self-Presentation among Douyu Live Streamers

Live streaming platforms, as computer-mediated communication (CMC) systems, provide streamers with a range of tools, such as webcams, beauty filters, and stream titles, to shape their online personas in ways that either conform to or deviate from viewers' expectations. Drawing on gender role and CMC theories, this study examines how streamers leverage CMC self-presentation tools to fulfill gender role expectations and their associated live streaming outcomes. Analyzing data collected from 867 streamers and 94,227 streams on Douyu, a popular Chinese live streaming platform, we find that although both female and male streamers make extensive use of CMC tools, female streamers are more likely to employ visual strategies, such as webcams and beauty filters, than male streamers. We further find that although different tools have varying associations on streamers' earnings and audience engagement, the benefits of webcam use are weaker for female than for male streamers. These findings underscore the complex interplay between gender roles and technology use in the live streaming domain.

cs.SI↗

arXiv2026

Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts

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.

cs.HC↗

arXiv2026

Closing the Motivation Gap: Incentives Enhance Visual Misinformation Discernment and Verification

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.

cs.HC↗

arXiv2026

From Verification to Amplification: Auditing Reverse Image Search as Algorithmic Gatekeeping in Visual Misinformation Fact-checking

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.

cs.SI↗

arXiv2025

Large Language Model-Informed Feature Discovery Improves Prediction and Interpretation of Credibility Perceptions of Visual Content

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.

cs.CV↗

arXiv2025

Crafting Synthetic Realities: Examining Visual Realism and Misinformation Potential of Photorealistic AI-Generated Images

Advances in generative models have created Artificial Intelligence-Generated Images (AIGIs) nearly indistinguishable from real photographs. Leveraging a large corpus of 30,824 AIGIs collected from Instagram and Twitter, and combining quantitative content analysis with qualitative analysis, this study unpacks AI photorealism of AIGIs from four key dimensions, content, human, aesthetic, and production features. We find that photorealistic AIGIs often depict human figures, especially celebrities and politicians, with a high degree of surrealism and aesthetic professionalism, alongside a low degree of overt signals of AI production. This study is the first to empirically investigate photorealistic AIGIs across multiple platforms using a mixed-methods approach. Our findings provide important implications and insights for understanding visual misinformation and mitigating potential risks associated with photorealistic AIGIs. We also propose design recommendations to enhance the responsible use of AIGIs.

cs.CY↗

arXiv2024

Examining Platformization in Cultural Production: A Comparative Computational Analysis of Hit Songs on TikTok and Spotify

The (re)creation and distribution of cultural products such as music are increasingly shaped by digital platforms. This study explores how TikTok and Spotify, situated in different governance and user contexts, could influence digital music production and reception within each platform and between each other. Focusing on daily hit song charts as the embodiment of platformization, we collected and analyzed a two-year longitudinal dataset on TikTok and Spotify. We tested the relationships between elements of platformization and hit song popularity within each platform, and examined cross-platform influence flow. Results reveal significant differences in major label, genre, and content features among hit songs on TikTok and Spotify, which can be explained by their distinct platformization practices. We also found some evidence that hit song popularity on Spotify might precede that on TikTok. This study illustrates both the platform-specific mechanisms of TikTok and Spotify and their interconnectedness in the cultural production ecosystem.

cs.SI↗

arXiv2022

Social Live-Streaming Use & Well-being: Examining Participation, Financial Commitment, Social Capital, and Psychological Well-being on Twitch.tv

This study examines how active participation, financial commitment, and passive participation in the leading social live-streaming service, Twitch.tv, relate to individuals' psychological well-being. The three dimensions of social capital-structural, relational, and cognitive-as well as parasocial relationship are explored as mediators. Cross-sectional survey data from 396 respondents was analyzed by comparing two fully saturated structural equation models. Findings indicate actively participating in a favorite streamers' Chat is positively associated with increased well-being. Structural social capital, or having more social interaction ties, positively mediates the relationship between active participation and well-being, as well as financial commitment and well-being. Greater cognitive social capital, or shared values and goals with a favorite streamer, is related to decreased well-being. Parasocial relationship does not significantly mediate the relationship between use and well-being. Our results demonstrate the importance of tangible social ties over the perceived relationships or identification with a favorite streamer.

cs.SI↗

arXiv2021

This photograph has been altered: Testing the effectiveness of image forensic labeling on news image credibility

Despite the ubiquity and proliferation of images and videos in online news environments, much of the existing research on misinformation and its correction is solely focused on textual misinformation, and little is known about how ordinary users evaluate fake or manipulated images and the most effective ways to label and correct such falsities. We designed a visual forensic label of image authenticity, Picture-O-Meter, and tested the label's efficacy in relation to its source and placement in an experiment with 2440 participants. Our findings demonstrate that, despite human beings' general inability to detect manipulated images on their own, image forensic labels are an effective tool for counteracting visual misinformation.

cs.SI↗

arXiv2020

Conspiracy and debunking narratives about COVID-19 origination on Chinese social media: How it started and who is to blame

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.

cs.SI↗

arXiv2020

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China

Can public social media data be harnessed to predict COVID-19 case counts? We analyzed approximately 15 million COVID-19 related posts on Weibo, a popular Twitter-like social media platform in China, from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," which are reports of one's own and other people's symptoms and diagnosis related to COVID-19. We then modeled the predictive power of sick posts and other COVID-19 posts on daily case counts. We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to 14 days ahead of official statistics. But other COVID-19 posts did not have similar predictive power. For a subset of geotagged posts (3.10% of all retrieved posts), we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, it is crucial to sift through the contents and efficiently identify true signals from noise.

cs.SI↗