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AbstractModern election campaigns leverage social media and the networks within to get their messages directly out to the public. We use the theory of extended party networks to explore networks of engaged users who extensively amplify messages posted by political candidates. Using Twitter data from the Senate races in the U.S. 2018 midterm election, we build Twitter extended party networks out of dedicated amplifiers to understand how those engaged users associate with the candidate message amplification. Results show that certain super‐amplifiers have a disproportionately large impact on information flows, and that decentralized networks with higher rates of follower reciprocity are associated with higher rates of message diffusion. Our study also finds some support for the idea that super‐amplifiers engage in coordinated efforts to diffuse political messages.

Scholarship in the sociology of medicine has tended to characterize diagnosis as disruptive to one's self-concept. This categorization, though, requires reconsideration in light of public conversations about mental health and community building around neurocognitive conditions, particularly among youth online. Drawing upon Tan's notion of "biographical illumination" (BI), which describes how medical frameworks can enrich personal biographies, we explored the shifting nature of BI through the case of TikTok. Combining quantitative and qualitative methods, we argue that TikTok serves as a space to discuss diagnosis and refine one's sense of self as a result of diagnosis. However, such personal transformation is inseparable from the app's affordances, or what we term "algorithmically mediated biographical illumination." BI shapes TikTok as a platform, and TikTok informs BI as a psychosocial process, leading to what we call "platformed diagnosis." These findings have broader critical applications for the study of algorithms, disability, and digital platforms.