Algorithmic amplification and polarization in social media

Donghee Shin, Texas Tech University
Emily Y. Shin, Zayed University

Document Type

Article

Source of Publication

Telematics and Informatics

Publication Date

1-1-2024

Abstract

There is growing concerns about how social media circulate extreme viewpoints, fuels division, and fosters radicalization. TikTok's role in fostering radicalized content was examined by tracing how users become radicalized on TikTok and how its recommendation algorithms drive this radicalization. We identified the social, technological, and psychological factors that contribute to the radicalization of ideological biases on social media and proposed a conceptual lens through which to analyze and predict such radicalization. The results revealed that the pathways by which users access far-right content are manifold and that a large part of this can be ascribed to platform recommendations through a positive feedback loop. Our results are consistent with the proposition that the generation and adoption of extreme content on TikTok largely reflect the user’s input and interaction with a platform. We also discuss how trends in artificial intelligence (AI)-based content systems are forged by an intricate combination of user interactions, platform intentions, and the interplay dynamics of a broader AI ecosystem.

ISSN

0736-324X

Publisher

Elsevier BV

First Page

102049

Last Page

102049

Disciplines

Communication

Keywords

Algorithmic amplification, Polarization, Radicalization, TikTok, Artificial intelligence

Indexed in Scopus

no

Open Access

no

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