Abstract
Deception, also known as faking, is a critical issue when collecting data using questionnaires. As shown by previous studies, people have the tendency to fake their answers whenever they gain an advantage from doing so, e.g., when taking a test for a job application. Current methods identify the general attitude of faking but fail to identify faking patterns and the exact responses affected. Moreover, these strategies often require extensive data collection of honest responses and faking patterns related to the specific questionnaire use case, e.g., the position that people are applying to. In this work, we propose a self-attention-based autoencoder (SABA) model that can spot faked responses in a questionnaire solely relying on a set of honest answers that are not necessarily related to its final use case. We collect data relative to a popular personality test (the 10-item Big Five test) in three different use cases, i.e., to obtain: (i) child custody in court, (ii) a position as a salesperson, and (iii) a role in a humanitarian organization. The proposed model outperforms by a sizeable margin in terms of F1 score three competitive baselines, i.e., an autoencoder based only on feedforward layers, a distribution model, and a k-nearest-neighbor-based model.
Highlights
IntroductionWe address the following unsolved issue: how can we identify when a subject is faking a specific question in a questionnaire?
In this paper, we address the following unsolved issue: how can we identify when a subject is faking a specific question in a questionnaire? Questionnaires have a widespread usage in the Web and are ubiquitously used to collect information that is usually assumed to be genuine
We propose a self-attention-based autoencoder (SABA) model that can spot faked responses in a questionnaire solely relying on a set of honest answers that are not necessarily related to its final use case
Summary
We address the following unsolved issue: how can we identify when a subject is faking a specific question in a questionnaire? For a variety of reasons, people frequently hide their true responses and the collected data cannot be reliably evaluated. This problem arises because responses to direct questions—such as “Have you ever thought about suicide?”—are faked in order to achieve an advantage—e.g., during a job application, applicants may want to hide their emotional instability. Deception to direct questions may take two different forms: faking-bad and fakinggood. Faking-bad characterizes some forensic settings (e.g., criminal, insurance claims) in which the examinee is likely to exaggerate or make up his/her psychological disorder [1,2]. Instead, when faking-good, respondents reduce undesirable aspects to present themselves in more acceptable ways
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