Algorithms not to blame for social media toxicity.
The article discusses research indicating that the polarizing effects of social media are inherent to the platforms' fundamental design rather than solely driven by algorithms. Researchers at the University of Amsterdam created 500 AI chatbots to simulate various political beliefs and observed their...
| Publicado en: | New Scientist Vol. 267; no. 3557; p. 14 |
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| Autor principal: | |
| Formato: | Journal Article |
| Publicado: |
New Scientist Ltd.
8/23/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187418834&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187418834 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02624079 NSI jtl: New Scientist issn: 02624079 maglogo: N pubinfo: dt: 8/23/2025 vid: 267 iid: 3557 pid: 39714 pub: New Scientist Ltd. artinfo: ui: 187418834 10.1016/s0262-4079(25)01365-x 187418834 ppf: 14 ppct: 0 formats: tig: atl: Algorithms not to blame for social media toxicity. aug: au: Stokel-Walker, Chris sug: ab: The article discusses research indicating that the polarizing effects of social media are inherent to the platforms' fundamental design rather than solely driven by algorithms. Researchers at the University of Amsterdam created 500 AI chatbots to simulate various political beliefs and observed their interactions on a simplified social network. The findings revealed that users with partisan views attracted more followers and engagement, suggesting that the core mechanics of posting, reposting, and following contribute to polarization. Various proposed interventions to mitigate this polarization showed minimal effectiveness, indicating that more profound changes may be necessary to address the issue. pubtype: Periodical doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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