Algorithmic Cultivation: How Social Media Feeds Shape User Language
Olivia Pal, Agam Goyal, Eshwar Chandrasekharan, and Koustuv Saha

TL;DR
This study investigates how algorithmic social media feeds influence users' language, revealing significant stylistic and semantic shifts driven by feed exposure, with effects varying by feed type and user engagement.
Contribution
It provides empirical evidence that social media feeds act as linguistic environments shaping user language over time, extending Cultivation Theory to online linguistic behavior.
Findings
Users exposed to different feeds show increased stylistic and semantic alignment.
Blacksky feed causes the most profound psycholinguistic changes.
Reposting behavior predicts linguistic convergence across feeds.
Abstract
Algorithmic feeds have become primary environments for encountering information online, yet while they shape what people see, less is known about how sustained feed exposure shapes how people write. Drawing on Cultivation Theory, we examine whether algorithmic feeds function as online environments that leave measurable traces in users' language. We leverage a large-scale longitudinal dataset of 235M posts by 4M users on Bluesky, and conduct a quasi-experimental study matching an initial pool of 368,513 users exposed to one of three feeds -- News, Science, and Blacksky -- with a pool of 2,001,915 active control users who did not engage with any of these feeds. We examine linguistic evolution across three dimensions: lexico-semantics, psycholinguistics, and topics. We find that users exposed to these feeds show significantly greater stylistic accommodation, semantic alignment, and…
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