Bio

Sunny Xun Liu is a Research Scholar and the Director of Research at Stanford Social Media Lab. Liu earned her Ph.D. in Mass Communication and Media from Michigan State University. Her research focuses on the social and psychological effects of social media and AI, social media and well-being, digital literacy, how the design of social robots and AI impact psychological perceptions. She has won top3 faculty paper awards from ICA and AEJMC and published in communication and psychology journals. She has served on the Chinese Communication Association’s Steering Committee and the ICA and AEJMC Research Chair and a steering committee member on PRISM (Promoting Research in Social Media and Health Symposium). Her research has been funded by NSF, Army Research Office, Google Research and Stanford HAI and has been published in multiple psychology and communication journals.

Academic Appointments

  • Associate Director for Research, Cyber Policy Center

Links

2026-27 Courses

All Publications

  • Topic-sensitive differentiation and algorithmic equivalence on short-form video platforms: A systematic review of visual frameworks COMPUTERS IN HUMAN BEHAVIOR REPORTS Johnston, E., Liu, S. 2026; 23

    View details for DOI 10.1016/j.chbr.2026.101214

    View details for Web of Science ID 001831401900001

  • Democratic governance through DAO-based deliberation and voting for inclusive decision making in AI models. Scientific reports Sharma, T., Potter, Y., Park, J., Liu, Y., Huang, Y., Liu, S., Song, D., Hancock, J., Wang, Y. 2026

    Abstract

    A major criticism of AI development is the lack of transparency, such as, inadequate documentation and traceability in its design and decision-making processes, leading to adverse outcomes including discrimination, lack of inclusivity and representation, and breaches of legal regulations. Underserved populations, in particular, are disproportionately affected by these design decisions. Furthermore, traditional social science techniques such as interviews, focus groups, and surveys struggle to adequately capture user needs and expectations in the digital era, due to their inherent limitations in deliberation, consensus-building, and providing consistent insights. We developed a democratic decision framework utilizing Decentralized Autonomous Organization (DAO) to enable underserved groups to deliberate and reach a consensus on key AI issues. To assess our proposed democratic decision mechanism, we conducted a case study on updating AI model specification based on diverse stakeholders input. We focus on reducing stereotypical biases in text-to-image systems, particularly gender bias in image generation from text prompts. We designed and experimented various governance configurations, including decision aggregation schemes and decision power, to examine how democratic processes could guide updates to AI model. Through a 2 × 2 experimental design, we tested various aggregation schemes (ranked vs. quadratic) and decision power distribution (equal vs. 20/80 differential) in a randomized online experiment (n=177) with participants from the global south and people with disabilities, to study how the varying governance mechanisms impact people's perceptions of the decision-making processes and resulting output of the AI Model specification. Our results indicate that despite their diverse backgrounds, participants showed convergence in deliberations on several aspects, including user control over image generation, multiple output options for user selection, and the social appropriateness and accuracy of generated images. Our study underscores the importance of use of appropriate governance in democratic decision-making in AI alignment. Notably, the combination of quadratic preference aggregation method which gives minorities more voice and equal decision power distribution, was perceived as a fairer and democratic approach.

    View details for DOI 10.1038/s41598-026-40180-8

    View details for PubMedID 41775822

  • "AI-Induced Delusional Spirals': Understanding Lived Experiences During Maladaptive Human-Chatbot Interactions Yang, Y., Schoenwald, S. K., Moore, J., Ong, D. C., Liu, S., Hancock, J. T., ACM ASSOC COMPUTING MACHINERY. 2026

    View details for DOI 10.1145/3772363.3798453

    View details for Web of Science ID 001792737300233

  • User-Centric Definitions of Quality Content and Engagement on Instagram Stevic, A., Wang, Y., Liu, S., Hancock, J., ACM ASSOC COMPUTING MACHINERY. 2026

    View details for DOI 10.1145/3772363.3798726

    View details for Web of Science ID 001792737300464

  • Of Loving and Losing: The Influence of Dating App Motivations and Perceived Success on Psychological Well-Being SOCIAL MEDIA + SOCIETY Stevic, A., Lee, A. Y., Liu, S., Hancock, J. 2025; 11 (2)

    View details for DOI 10.1177/20563051251346888

    View details for Web of Science ID 001517512300001

  • Complexity of Agency in VR Learning Environments: Exploring Associations with Interactivity, Learning Outcomes, and Affect McGivney, E., Queiroz, A. C. M., Miller, M., Liu, S., Beams, B., Han, E., Woolsey, E. S., Frazier, K., Petersen, X., Hancock, J., Bailenson, J. edited by Kruger, J. M., Pedrosa, D., Beck, D., Bourguet, M. L., Dengel, A., Ghannam, R., Miller, A., Pena-Rios, A., Richter, J. SPRINGER INTERNATIONAL PUBLISHING AG. 2025: 65-79

    View details for DOI 10.1007/978-3-031-80475-5_5

    View details for Web of Science ID 001460294700005

  • Collaboration, crowdsourcing, and misinformation. PNAS nexus Jia, C., Lee, A. Y., Moore, R. C., Decatur, C. H., Liu, S. X., Hancock, J. T. 2024; 3 (10): pgae434

    Abstract

    One of humanity's greatest strengths lies in our ability to collaborate to achieve more than we can alone. Just as collaboration can be an important strength, humankind's inability to detect deception is one of our greatest weaknesses. Recently, our struggles with deception detection have been the subject of scholarly and public attention with the rise and spread of misinformation online, which threatens public health and civic society. Fortunately, prior work indicates that going beyond the individual can ameliorate weaknesses in deception detection by promoting active discussion or by harnessing the "wisdom of crowds." Can group collaboration similarly enhance our ability to recognize online misinformation? We conducted a lab experiment where participants assessed the veracity of credible news and misinformation on social media either as an actively collaborating group or while working alone. Our results suggest that collaborative groups were more accurate than individuals at detecting false posts, but not more accurate than a majority-based simulated group, suggesting that "wisdom of crowds" is the more efficient method for identifying misinformation. Our findings reorient research and policy from focusing on the individual to approaches that rely on crowdsourcing or potentially on collaboration in addressing the problem of misinformation.

    View details for DOI 10.1093/pnasnexus/pgae434

    View details for PubMedID 39430219

    View details for PubMedCentralID PMC11488513

  • Effects of Using Artificial Intelligence on Interpersonal Perceptions of Job Applicants. Cyberpsychology, behavior and social networking Weiss, D., Liu, S. X., Mieczkowski, H., Hancock, J. T. 1800

    Abstract

    Text-based artificial intelligence (AI) systems are increasingly integrated into a host of interpersonal domains. Although decision-making and person perception in hiring and employment opportunities have been an area of psychological interest for many years, only recently have scholars begun to investigate the role that AI plays in this context. To better understand the impact of AI in employment-related contexts, we conducted two experiments investigating how the use of AI by applicants influences their job opportunities. In our preregistered Study 1, we examined how a prospective job applicants' use of AI, as well as their language status (native English speaker or non-native English speaker), influenced participants' impressions of their warmth, competence, social attractiveness, and hiring desirability. In Study 2, we examined how receiving assistance impacted interpersonal perceptions, and how perceptions might change whether the help was provided by AI or by another human. The results from both experiments suggest that the use of AI technologies can negatively influence perceptions of jobseekers. This negative impact may be grounded in the perception of receiving any type of help, whether it be from a machine or a person. These studies provide additional evidence for the Computers as Social Actors framework and advance our understanding of AI-Mediated Communication. The results also raise questions about transparency and deception related to AI use in interpersonal contexts.

    View details for DOI 10.1089/cyber.2020.0863

    View details for PubMedID 35021895

  • Not All AI are Equal: Exploring the Accessibility of AI-Mediated Communication Technology COMPUTERS IN HUMAN BEHAVIOR Goldenthal, E., Park, J., Liu, S. X., Mieczkowski, H., Hancock, J. T. 2021; 125

    View details for DOI 10.1016/j.chb.2021.106975

    View details for Web of Science ID 000690868900011

  • Can a social robot be too warm or too competent? Older Chinese adults' perceptions of social robots and vulnerabilities COMPUTERS IN HUMAN BEHAVIOR Liu, S., Shen, Q., Hancock, J. 2021; 125

    View details for DOI 10.1016/j.chb.2021.106942

    View details for Web of Science ID 000690868900012

  • Helping Not Hurting: Applying the Stereotype Content Model and BIAS Map to Social Robotics Mieczkowski, H., Liu, S., Hancock, J., Reeves, B., IEEE IEEE. 2019: 222–29

    View details for Web of Science ID 000467295400029

  • How People Form Folk Theories of Social Media Feeds and What It Means for How We Study Self-Presentation DeVito, M. A., Birnholtz, J., Hancock, J. T., French, M., Liu, S., ACM ASSOC COMPUTING MACHINERY. 2018

    View details for DOI 10.1145/3173574.3173694

    View details for Web of Science ID 000509673101045