Evidence map›Paper›PMID 37985637›Full record

ArticleBehavior research methods2024

Model-agnostic unsupervised detection of bots in a Likert-type questionnaire.

Michael John Ilagan, Carl F Falk

Abstract read
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In one paragraph

Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Michael John IlaganDepartment of Psychology, McGill University, 2001 McGill College, 7th Floor, H3A 1G1, Montreal, QC, Canada.
Carl F FalkDepartment of Psychology, McGill University, 2001 McGill College, 7th Floor, H3A 1G1, Montreal, QC, Canada. carl.falk@mcgill.ca.

Funding

Fonds de recherche du Québec - Nature et technologies 2022-PR-298903Natural Sciences and Engineering Research Council of Canada DGECR-2018-00083,RGPIN-2018-05357
6 · The paper itself

Abstract

To detect bots in online survey data, there is a wealth of literature on statistical detection using only responses to Likert-type items. There are two traditions in the literature. One tradition requires labeled data, forgoing strong model assumptions. The other tradition requires a measurement model, forgoing collection of labeled data. In the present article, we consider the problem where neither requirement is available, for an inventory that has the same number of Likert-type categories for all items. We propose a bot detection algorithm that is both model-agnostic and unsupervised. Our proposed algorithm involves a permutation test with leave-one-out calculations of outlier statistics. For each respondent, it outputs a p value for the null hypothesis that the respondent is a bot. Such an algorithm offers nominal sensitivity calibration that is robust to the bot response distribution. In a simulation study, we found our proposed algorithm to improve upon naive alternatives in terms of 95% sensitivity calibration and, in many scenarios, in terms of classification accuracy.

Indexed as

AlgorithmsComputer SimulationData Interpretation, StatisticalHumansModels, StatisticalSurveys and QuestionnairesAberrant respondingBotsOutlier detectionPermutation testUnsupervised learning

Identifiers

PMID37985637

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.