How online studies must increase their defences against AI

LLM agents can now pass as human participants, threatening the validity of online social science. We urge a shift from ad-hoc checks to multi-layered, adaptive defenses, borrowing from internet anti-bot practice, and call for cooperation across researchers, platforms, and institutions, to guard against this challenge. LLM agents can now pass as human participants, threatening the validity of online social science. We urge a shift from ad-hoc checks to multi-layered, adaptive defenses, borrowing from internet anti-bot practice, and call for cooperation across researchers, platforms, and institutions, to guard against this challenge.

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Publication Details

Journal
Communications Psychology
Published
2026-01-07
DOI
https://doi.org/10.1038/s44271-025-00388-2
Citations
1
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
49.38
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article

How online studies must increase their defences against AI

Frank Papenmeier, Markus Huff, Gerrit Anders, Jürgen Buder
1 citations
Communications Psychology
Computational and Text Analysis Methods
49.38
article

How online studies must increase their defences against AI

Frank Papenmeier, Markus Huff, Gerrit Anders, Jürgen Buder
article en
1 citations

Abstract

LLM agents can now pass as human participants, threatening the validity of online social science. We urge a shift from ad-hoc checks to multi-layered, adaptive defenses, borrowing from internet anti-bot practice, and call for cooperation across researchers, platforms, and institutions, to guard against this challenge. LLM agents can now pass as human participants, threatening the validity of online social science. We urge a shift from ad-hoc checks to multi-layered, adaptive defenses, borrowing from internet anti-bot practice, and call for cooperation across researchers, platforms, and institutions, to guard against this challenge.

Communications PsychologyVol. 4(1)
Leibniz-Institut für Wissensmedien (DE), University of Tübingen (DE)
Openalex Percentile: Top 1%
Computational and Text Analysis Methods
49.38
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