Algorithmic indifference

The dearth of news recommendations on TikTok

Datos Bibliográficos

ID6443397
AutoresNick Hagar (0000-0001-5110-3737, Northwestern University, autor de correspondencia), Nicholas Diakopoulos (0000-0001-5005-6123, Northwestern University)
Año2023
Volumen27
Número6
Páginas3449-3469
Fecha de publicación2023-08-30
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaNew Media & Society (JOURNAL)
Identificadores de la revistaISSN: 1461-4448 • E-ISSN: 1461-7315
EditorialSAGE Publishing (PUBLISHER • US)
DOI10.1177/14614448231192964
OpenAlexW4386291252
IdiomaEN
Citas recibidas20
Referencias citadas26

The role of recommendation systems in news consumption has been hotly contested. From one perspective, the combination of personalized recommendations and practically limitless content diminishes news consumption, as people turn to more entertaining fare. From another, algorithmic systems and social networks heighten incidental exposure, raising opportunities for news consumption regardless of explicit individual interest. In this work, we examine the potential for algorithmic exposure to news on TikTok, a massively popular social network built around short-form video. In the context of US-based news audiences, we examine the accounts TikTok recommends, the videos it shows new users, and its trending hashtags. We find almost no evidence of proactive news exposure on TikTok’s behalf. We also find that, while TikTok’s algorithms respond slightly to active signals of news interest from simulated users, that response does not lead to increased exposure to credible news content. These findings highlight a lack of algorithmic news distribution on TikTok

Advertising · Business · Consumption (sociology · Context (archaeology · Internet privacy · News media · Perspective (graphical · Sociology · Computer Science · History · Misinformation and Its Impacts · Social Media and Politics · Artificial Intelligence