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

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.

Automated summary

This article introduces a model-agnostic and unsupervised bot detection algorithm that uses permutation test with leave-one-out calculations of outlier statistics. The algorithm provides a p value for each respondent to determine if they are a bot. The simulation study shows that the proposed algorithm outperforms naive alternatives in terms of sensitivity calibration and classification accuracy.