The internet is becoming increasingly visual, but social computing research and methodological training has relied heavily on textual methods. Methodological innovation is needed to study visual social data, including problematic information (mis- and disinformation, propaganda, hate, AI slop, etc). Contending with this, we present a framework for conducting grounded, interpretive, computationally supported, mixed-method research on collections of visual social media data. We developed this framework while grappling with the ethical, logistical, and methodological challenges of conducting in-depth analysis of potentially harmful visual content while caring for our research team. We document our framework components of visual grammars, human analysis, and computationally supported analysis with an umbrella commitment to care and its use in three empirical case studies. We also provide recommendations and implications for the HCI community in embracing training in and the advancing of visual methods and research, including a sensitizing concept of visual integrity.

CHI ’25, Yokohama, Japan

Along with other visual content, data visualizations are increasingly used within online discourse, including political communication. Though often considered to be ''objective'', data visualizations can also be created and/or appropriated to mislead. Here, we study the use and evolution of data visualizations within social media discourse around the ongoing ''crisis'' at the US-Mexico border in 2024. Through computationally-assisted qualitative analysis, we first describe how data visualizations are used to support four anti-immigrant frames, highlighting key tactics and sources of these visualizations. Next, we conduct a deep analysis of three Data Visualization Lineages (DVLs), exploring the role of adaptations, annotations, and remixing within families of data visualizations that share the same origin but have diverged through distinct visual alterations. We conclude by discussing approaches for supporting researchers in identifying and unpacking data visualization lineages, and highlighting design opportunities for mitigating the impact of misleading data visualizations in online discourse.

Though research into text-to-image generators (T2Is) such as Stable Diffusion has demonstrated their amplification of societal biases and potentials to cause harm, such research has primarily relied on computational methods instead of seeking information from real users who experience harm, which is a significant knowledge gap. In this paper, we conduct the largest human subjects study of Stable Diffusion, with a combination of crowdsourced data from 133 crowdworkers and 14 semi-structured interviews across diverse countries and genders. Through a mixed-methods approach of intra-set cosine similarity hierarchies (i.e., comparing multiple Stable Diffusion outputs for the same prompt with each other to examine which result is `closest' to the prompt) and qualitative thematic analysis, we first demonstrate a large disconnect between user expectations for Stable Diffusion outputs with those generated, evidenced by a set of Stable Diffusion renditions of `a Person' providing images far away from such expectations. We then extend this finding of general dissatisfaction into highlighting representational harms caused by Stable Diffusion upon our subjects, especially those with traditionally marginalized identities, subjecting them to incorrect and often dehumanizing stereotypes about their identities. We provide recommendations for a harm-aware approach to (re)design future versions of Stable Diffusion and other T2Is.

The Latinx diaspora in the United States is a rapidly growing and complex demographic who face intersectional harms and marginalizations in sociotechnical systems and are currently underserved in CSCW research. While the field understands that algorithms and digital content are experienced differently by marginalized populations, more investigation is needed about how Latinx people experience social media and, in particular, visual media. In this paper, we focus on how Latinx people experience the algorithmic system of the video-sharing platform TikTok. Through a bilingual interview and visual elicitation study of 19 Latinx TikTok users and 59 survey participants, we explore how Latinx individuals experience TikTok and its Latinx content. We find Latinx TikTok users actively use platform affordances to create positive and affirming identity content feeds, but these feeds are interrupted by negative content (i.e. violence, stereotypes, linguistic assumptions) due to platform affordances that have unique consequences for Latinx diaspora users. We discuss these implications on Latinx identity and representation, introduce the concept of algorithmic identity compression, where sociotechncial systems simplify, flatten, and conflate intersection identities, resulting in compression via the loss of critical cultural data deemed unnecessary by these systems and designers of them. This study explores how Latinx individuals are particularly vulnerable to this in sociotechnical systems, such as, but not limited to, TikTok.