RESEARCH

My scholarship is at the intersection of computational social science, visual media, science communication, and social media analytics. At its core, my work aims to shed light on how digital media is transforming our communication and information environment. This inquiry encompasses a diverse array of themes, including the rising importance of visual content and visual-oriented platforms, the pivotal role of social media metrics in content creation, the emergence of AI-generated media, and their collective influence on journalistic norms, public opinions, and user behaviors.

Area: Visual Communication, Computer Vision, Visual Politics, Visual Misinformation

Visual media is ubiquitous in today’s information landscape, presenting a unique analytical challenge to the field of computational social science. I use a multi-disciplinary approach that combines computer vision techniques, surveys, and experiments to investigate the production, diffusion, and impact of visual messages across diverse communication contexts. My prior works have examined depictions of political figures, fitspiration imagery, and representations of climate change. My current research examines how attributes of misinformation claims in visual formats, such as memes, infographics, and synthetic media, affect viewers’ perceptions of credibility. Additionally, I advance computational methods for visual analysis by creating open-source computer packages and libraries that enable other researchers to easily incorporate computational visual analysis into their own work.

  • Peng, Y., Lu, Y., & Shen, C. (2023). An agenda for studying credibility perceptions of visual misinformation. Political Communication. [LINK]
  • Sharma, M., & Peng, Y. (2023). How visual aesthetics and calorie density predict food image popularity on Instagram: A computer vision analysis. Health Communication. [LINK]
  • Zhang, H., & Peng, Y. (2022). Image clustering: An unsupervised approach to categorize visual data in social science research. Sociological Methods & Research. [LINK]
  • Peng, Y. (2022). Athec: A Python library for computational aesthetic analysis of visual media in social science research. Computational Communication Research, 4(1). [LINK]
  • Murashka, V., Liu, J., & Peng, Y. (2021). Fitspiration on Instagram: Identifying topic clusters in user comments to posts with objectification features. Health Communication, 36(12), 1537-1548. [LINK]
  • Peng, Y. (2021). What makes politicians’ Instagram posts popular? Analyzing social media strategies of candidates and office holders with computer vision. The International Journal of Press/Politics, 26(1), 143-166. [LINK]
  • Peng, Y. (2018). Same candidates, different faces: Uncovering media bias in visual portrayals of presidential candidates with computer vision. Journal of Communication, 68(5), 920-941. [LINK]
  • Peng, Y. (2017). Time travel with one click: Effects of digital filters on perceptions of photographs. In Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (pp. 6000-6011). [LINK]
Area: Computational Social Science, Social Media Metrics, News Consumption, News Engagement

My scholarship in computational social science explores news consumption and engagement in the attention economy. I am particularly interested in studying how media outlets navigate the incentives of pursuing virality and capturing audience attention, and the subsequent impact on our information environment and the foundations of democratic discourse. My prior works have investigated how today’s news consumers engage with digital information, how media outlets employ various content strategies to optimize audience attention and growth, and how social media algorithms allocate audience attention across media outlets of different sizes. To tackle these complex research questions, I employ multiple analytical approaches, including natural language processing, time series modeling, and social network analysis. Moreover, my interest extends to the pursuit of generating causal claims from observational data embedded within digital traces, which helps unravel the intricate interplay between digital technologies, news production, and audience behaviors.

  • Peng, Y., Yang, T., & Fang, K. (2023). The dark side of entertainment? How viral entertaining media build an attention base for the far-right politics of The Epoch Times. New Media & Society. [LINK]
  • Mukerjee, S., Yang, T., & Peng, Y. (2023). Metrics in action: How social media metrics shape news production on Facebook. Journal of Communication. [LINK]
  • Peng, Y., & Yang, T. (2022). Anatomy of audience duplication networks: How individual characteristics differentially contribute to fragmentation in news consumption and trust. New Media & Society, 24(10), 2270-2290. [LINK]
  • Yang, T., & Peng, Y. (2022). The importance of trending topics in the gatekeeping of social media news engagement: A natural experiment on Weibo. Communication Research, 49(7), 994-1015. [LINK]
Area: Science Communication, Public Understanding of AI, Political Polarization of Science

I study science communication, with a focus on public perceptions of artificial intelligence technologies, such as facial recognition and self-driving cars. AI applications could profoundly change our ways of working and living. I am particularly interested in how emerging AI technologies are connected to debates about inequality and social justice and how these factors affect public opinion. In the wake of the pandemic, I have also developed a line of research on the political polarization of science, which demonstrates the importance of ideologies and worldviews in shaping our responses to issues such as Covid-19, vaccination, and AI applications.

  • Peng, Y. (2023). The role of ideological dimensions in shaping acceptance of facial recognition technology and reactions to algorithm bias. Public Understanding of Science, 32(2), 190-207. [LINK]
  • Peng, Y. (2022). Give me liberty or give me COVID‐19: How social dominance orientation, right‐wing authoritarianism, and libertarianism explain Americans’ reactions to COVID‐19. Risk Analysis, 42(12), 2691-2703. [LINK]
  • Peng, Y. (2022). Politics of COVID-19 vaccine mandates: Left/right-wing authoritarianism, social dominance orientation, and libertarianism. Personality and Individual Differences, 194, 111661. [LINK]
  • Peng, Y. (2020). The ideological divide in public perceptions of self-driving cars. Public understanding of science, 29(4), 436-451. [LINK]
  • Kohl, P. A., Kim, S. Y., Peng, Y., Akin, H., Koh, E. J., Howell, A., & Dunwoody, S. (2016). The influence of weight-of-evidence strategies on audience perceptions of (un) certainty when media cover contested science. Public Understanding of Science, 25(8), 976-991. [LINK]