Algorithmic Cultivation: How Social Media Feeds Shape User Language

2026-05-16

AI Summary

This study investigates whether and how algorithmically curated information feeds shape users’ linguistic expression over time. Grounded in cultivation theory, it leverages longitudinal data from 4 million users and 235 million posts on the Bluesky platform, employing a quasi-experimental design to compare language evolution—across lexical semantics, psycholinguistic features, and topical dimensions—between users exposed to News, Science, or Blacksky feeds and a control group. Extending cultivation theory to linguistic behavior for the first time, the work demonstrates that algorithmic feeds act as persistent linguistic environments that systematically mold writing styles. Integrating large-scale longitudinal analysis, matching methods, regression modeling, and NLP techniques, the study reveals significant convergence among exposed users in stylistic adaptation, semantic alignment, and register formalization. Notably, Blacksky induces the strongest psycholinguistic restructuring, while News and Science primarily influence register and topic; retweeting emerges as the most stable predictor of cross-feed linguistic convergence.

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 register formalization than matched controls. These effects vary markedly by feed identity -- Blacksky produces the deepest psycholinguistic restructuring, with significant shifts in cognitive processing, affective expression, and pronoun use, while News and Science effects are largely confined to register and topical focus. Regression models reveal that reposting is the most consistent predictor of linguistic convergence across all feeds, whereas posting and bookmarking show feed-dependent effects, with effects differing more than fourfold across feeds. Our work extends Cultivation Theory beyond belief formation to linguistic behavior, demonstrating that feeds function as persistent linguistic environments that gradually shape what and how users write online. Our work has implications for studying algorithmic influence, online identity formation, and the design and governance of feed-based platforms that mediate online interactions.

Problem

• algorithmic feeds
• linguistic evolution
• Cultivation Theory
• social media
• user language

Innovation

• algorithmic feeds
• linguistic accommodation
• cultivation theory
• quasi-experimental design
• longitudinal analysis