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dc.contributor.authorRoman, Zachary Joseph
dc.contributor.authorBrandt, Holger
dc.contributor.authorMiller, Jason Michael
dc.date.accessioned2022-07-11T19:27:50Z
dc.date.available2022-07-11T19:27:50Z
dc.date.issued2022-04-27
dc.identifier.citationRoman ZJ, Brandt H and Miller JM (2022) Automated Bot Detection Using Bayesian Latent Class Models in Online Surveys. Front. Psychol. 13:789223. doi: 10.3389/fpsyg.2022.789223en_US
dc.identifier.urihttp://hdl.handle.net/1808/32822
dc.description.abstractBehavioral scientists have become increasingly reliant on online survey platforms such as Amazon's Mechanical Turk (Mturk). These platforms have many advantages, for example it provides ease of access to difficult to sample populations, a large pool of participants, and an easy to use implementation. A major drawback is the existence of bots that are used to complete online surveys for financial gain. These bots contaminate data and need to be identified in order to draw valid conclusions from data obtained with these platforms. In this article, we will provide a Bayesian latent class joint modeling approach that can be routinely applied to identify bots and simultaneously estimate a model of interest. This method can be used to separate the bots' response patterns from real human responses that were provided in line with the item content. The model has the advantage that it is very flexible and is based on plausible assumptions that are met in most empirical settings. We will provide a simulation study that investigates the performance of the model under several relevant scenarios including sample size, proportion of bots, and model complexity. We will show that ignoring bots will lead to severe parameter bias whereas the Bayesian latent class model results in unbiased estimates and thus controls this source of bias. We will illustrate the model and its capabilities with data from an empirical political ideation survey with known bots. We will discuss the implications of the findings with regard to future data collection via online platforms.en_US
dc.publisherFrontiers Mediaen_US
dc.rights© 2022 Roman, Brandt and Miller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectLatent class analysisen_US
dc.subjectMixture modelsen_US
dc.subjectStructural equation modelsen_US
dc.subjectMTurken_US
dc.subjectBotsen_US
dc.titleAutomated Bot Detection Using Bayesian Latent Class Models in Online Surveysen_US
dc.typeArticleen_US
kusw.kuauthorMiller, Jason Michael
kusw.kudepartmentPsychologyen_US
dc.identifier.doi10.3389/fpsyg.2022.789223en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.identifier.pmid35572225en_US
dc.rights.accessrightsopenAccessen_US


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© 2022 Roman, Brandt and Miller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
Except where otherwise noted, this item's license is described as: © 2022 Roman, Brandt and Miller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).