AI discussion spreads differently across online groups on Bluesky
Group dynamics of engagement with AI topics on Bluesky
Social and Information NetworksComputers and Society
Summary
Social media is an important place where people talk about big topics like artificial intelligence (AI). The authors looked at how different groups on a social media platform called Bluesky reacted to a new AI release. They found that some groups responded strongly right away, while others spread interest more through their social connections. This means that online engagement with AI topics happens in different ways depending on the group, and just looking at total activity can hide these differences.
What this means in practice
- •For social media analysts: Identify how different online groups respond to current AI events to improve targeted communication strategies.
- •For marketing teams: Tailor campaigns for AI products by understanding whether groups spread interest directly or through their networks.
Tested on one dataset.
Authors
Bradley Huynh, Moyi Tian, Nancy Rodríguez
Abstract
Social media increasingly shapes everyday life, serving as both a central venue for discussion of major events and a space where online collective behavior can spill over into real-world activity, while artificial intelligence (AI) is likewise becoming increasingly influential across society. Understanding how different online communities respond to AI-related events, and disentangling the mechanisms underlying the development of such engagement, are therefore increasingly important for studying information spreading and the societal reception of AI. Our work addresses the limited connection between empirical studies of AI-related online engagement and mathematical modeling of group-level spreading dynamics. We develop a framework to collect and organize empirical Bluesky activity into distinct user groups, then use a network-based dynamics model to investigate the mechanisms underlying their engagement. We use the release of DeepSeek R1 as a case study under two complementary grouping schemes: AI-related communities and academic disciplines. For each group, we fit a network-based Susceptible-Infected-Recovered (SIR)-type model augmented with an exogenous engagement term, allowing us to quantify the relative strengths of endogenous network-driven spreading and direct external response. Across groups, we find that strong direct responses to the event do not necessarily coincide with strong network-driven propagation, revealing distinct engagement patterns that may be obscured by aggregate activity alone.