Echo Chambers in the Age of Algorithms: An Audit of Twitter's Friend Recommender System
Web Science Conference(2024)
摘要
The presence of political misinformation and ideological echo chambers on
social media platforms is concerning given the important role that these sites
play in the public's exposure to news and current events. Algorithmic systems
employed on these platforms are presumed to play a role in these phenomena, but
little is known about their mechanisms and effects. In this work, we conduct an
algorithmic audit of Twitter's Who-To-Follow friend recommendation system, the
first empirical audit that investigates the impact of this algorithm in-situ.
We create automated Twitter accounts that initially follow left and right
affiliated U.S. politicians during the 2022 U.S. midterm elections and then
grow their information networks using the platform's recommender system. We
pair the experiment with an observational study of Twitter users who already
follow the same politicians. Broadly, we find that while following the
recommendation algorithm leads accounts into dense and reciprocal neighborhoods
that structurally resemble echo chambers, the recommender also results in less
political homogeneity of a user's network compared to accounts growing their
networks through social endorsement. Furthermore, accounts that exclusively
followed users recommended by the algorithm had fewer opportunities to
encounter content centered on false or misleading election narratives compared
to choosing friends based on social endorsement.
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