The Bots Ruining Social Science Are Not Bots at All

Researchers who employ online data collection from human subjects currently face a conundrum: It is both essential to how behavioral science functions and threatened by low-quality data. It is often assumed that random, inconsistent, and otherwise incomprehensible data in online surveys comes mainly from bots. Despite this assumption, few studies have directly examined where problematic data comes from, even though identifying the source has important implications for creating the right solutions. We examined this issue on several popular participant-recruitment platforms, including Mechanical Turk (MTurk) and Lucid. Across four studies spanning 5 years using multiple methods, we here provide evidence that most of the data-quality problems affecting online research using online panels can be tied to fraudulent users from outside of the United States-not bots. We identify many of the telltale signs that humans leave behind and describe the most effective ways of blocking problematic human responses to address the online data-quality problem.

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Publication Details

Journal
Perspectives on Psychological Science
Published
2026-01-06
DOI
https://doi.org/10.1177/17456916251404872
Primary Topic
Survey Methodology and Nonresponse
Type
article
Field-Weighted Citation Impact
0.00
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article

The Bots Ruining Social Science Are Not Bots at All

L M Litman, Shalom Noach Jaffe, Richa Gautam, Rachel Hartman et al.
Perspectives on Psychological Science
Survey Methodology and Nonresponse
article

The Bots Ruining Social Science Are Not Bots at All

L M Litman, Shalom Noach Jaffe, Richa Gautam, Rachel Hartman, Cheskie Rosenzweig, Moss Aj, Jonathan Robinson
article en

Abstract

Researchers who employ online data collection from human subjects currently face a conundrum: It is both essential to how behavioral science functions and threatened by low-quality data. It is often assumed that random, inconsistent, and otherwise incomprehensible data in online surveys comes mainly from bots. Despite this assumption, few studies have directly examined where problematic data comes from, even though identifying the source has important implications for creating the right solutions. We examined this issue on several popular participant-recruitment platforms, including Mechanical Turk (MTurk) and Lucid. Across four studies spanning 5 years using multiple methods, we here provide evidence that most of the data-quality problems affecting online research using online panels can be tied to fraudulent users from outside of the United States-not bots. We identify many of the telltale signs that humans leave behind and describe the most effective ways of blocking problematic human responses to address the online data-quality problem.

Perspectives on Psychological ScienceVol. 21(2)
Lander University (US), Fairleigh Dickinson University (US), Queens Public Library (US), Columbia University (US), University of Delaware (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 95%
Survey Methodology and Nonresponse
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