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.
Dhawka et al. (2025) studied this question.