Predicting faking in interviews with automated text analysis and personality
INTRODUCTION/PURPOSE: Some assessment companies are already applying automated text-analysis to job interviews. We aimed to investigate if text-mining software can predict faking in job interviews. To our knowledge, we are the first to examine the predictive validity of text-mining software to detec...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/75891 |
| _version_ | 1848763576307679232 |
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| author | Holtrop, Djurre Van Breda, Ward Oostrom, Janneke De Vries, Reinout |
| author_facet | Holtrop, Djurre Van Breda, Ward Oostrom, Janneke De Vries, Reinout |
| author_sort | Holtrop, Djurre |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | INTRODUCTION/PURPOSE: Some assessment companies are already applying automated text-analysis to job interviews. We aimed to investigate if text-mining software can predict faking in job interviews. To our knowledge, we are the first to examine the predictive validity of text-mining software to detect faking. DESIGN/METHOD: 140 students from the University of Western Australia were instructed to behave as an applicant. First, participants completed a personality questionnaire. Second, they were given 12 personality-based interview questions to read and prepare. Third, participants were interviewed for approximately 15-20 minutes. Finally, participants were asked to—honestly—indicate to what extent they had verbally (α=.93) and non-verbally (α=.77) faked during the interview. Subsequently, the interview text transcripts (M[words]=1,755) were automatically analysed with text-mining software in terms of personality-related words (using a program called Sentimentics) and 10 other hypothesised linguistic markers (using LIWC2015). RESULTS: Overall, the results showed very modest relations between verbal faking and the text-mining programs’ output. More specifically, verbal faking related to the linguistic categories ‘affect’ (r=.21) and ‘positive emotions’ (r=.21). Altogether, the personality-related words and linguistic markers predicted a small amount of variance in verbal faking (R2=.17). Non-verbal faking was not related to any of the text-mining programs’ output. Finally, self-reported personality was not related to any of the faking behaviours. LIMITATIONS/PRACTICAL IMPLICATIONS: The present study shows that linguistic analyses with text-mining software is unlikely to detect fakers accurately. Interestingly, verbal faking was only related to positive affect markers. ORIGINALITY/VALUE: This puts the use of text-analysis software on job interviews in question. |
| first_indexed | 2025-11-14T11:05:39Z |
| format | Conference Paper |
| id | curtin-20.500.11937-75891 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:05:39Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-758912019-07-08T05:33:01Z Predicting faking in interviews with automated text analysis and personality Holtrop, Djurre Van Breda, Ward Oostrom, Janneke De Vries, Reinout INTRODUCTION/PURPOSE: Some assessment companies are already applying automated text-analysis to job interviews. We aimed to investigate if text-mining software can predict faking in job interviews. To our knowledge, we are the first to examine the predictive validity of text-mining software to detect faking. DESIGN/METHOD: 140 students from the University of Western Australia were instructed to behave as an applicant. First, participants completed a personality questionnaire. Second, they were given 12 personality-based interview questions to read and prepare. Third, participants were interviewed for approximately 15-20 minutes. Finally, participants were asked to—honestly—indicate to what extent they had verbally (α=.93) and non-verbally (α=.77) faked during the interview. Subsequently, the interview text transcripts (M[words]=1,755) were automatically analysed with text-mining software in terms of personality-related words (using a program called Sentimentics) and 10 other hypothesised linguistic markers (using LIWC2015). RESULTS: Overall, the results showed very modest relations between verbal faking and the text-mining programs’ output. More specifically, verbal faking related to the linguistic categories ‘affect’ (r=.21) and ‘positive emotions’ (r=.21). Altogether, the personality-related words and linguistic markers predicted a small amount of variance in verbal faking (R2=.17). Non-verbal faking was not related to any of the text-mining programs’ output. Finally, self-reported personality was not related to any of the faking behaviours. LIMITATIONS/PRACTICAL IMPLICATIONS: The present study shows that linguistic analyses with text-mining software is unlikely to detect fakers accurately. Interestingly, verbal faking was only related to positive affect markers. ORIGINALITY/VALUE: This puts the use of text-analysis software on job interviews in question. 2019 Conference Paper http://hdl.handle.net/20.500.11937/75891 restricted |
| spellingShingle | Holtrop, Djurre Van Breda, Ward Oostrom, Janneke De Vries, Reinout Predicting faking in interviews with automated text analysis and personality |
| title | Predicting faking in interviews with automated text analysis and personality |
| title_full | Predicting faking in interviews with automated text analysis and personality |
| title_fullStr | Predicting faking in interviews with automated text analysis and personality |
| title_full_unstemmed | Predicting faking in interviews with automated text analysis and personality |
| title_short | Predicting faking in interviews with automated text analysis and personality |
| title_sort | predicting faking in interviews with automated text analysis and personality |
| url | http://hdl.handle.net/20.500.11937/75891 |