Deception Detection: Using Machine Learning to Analyze 911 Calls.
This study examined the use of machine learning in detecting deception among 210 individuals reporting homicides or missing persons to 911. The sample included an equal number of false allegation callers (FAC) and true report callers (TRC) identified through case adjudication. Independent coders, un...
| Publicado en: | Personality & Social Psychology Bulletin Vol. 52; no. 4; pp. 777 - 792 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Sage Publications Inc.
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=191984642&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 191984642 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 01461672 PAS jtl: Personality & Social Psychology Bulletin issn: 01461672 maglogo: Y pubinfo: dt: Apr2026 vid: 52 iid: 4 pid: 344 pub: Sage Publications Inc. artinfo: ui: 191984642 10.1177/01461672241287064 ppf: 777 ppct: 15 formats: tig: atl: Deception Detection: Using Machine Learning to Analyze 911 Calls. aug: au: Markey, Patrick M. Dapice, Jennie Berry, Brooke Slotter, Erica B. affil: Villanova University, Villanova, PA, USA su: Social cues Homicide investigation Lie detectors & detection Machine learning Random forest algorithms sug: subj: Social cues Homicide investigation Lie detectors & detection Investigation Services Machine learning Random forest algorithms keyword: 911 calls deception machine learning social behavior violent crime 911 calls deception machine learning social behavior violent crime ab: This study examined the use of machine learning in detecting deception among 210 individuals reporting homicides or missing persons to 911. The sample included an equal number of false allegation callers (FAC) and true report callers (TRC) identified through case adjudication. Independent coders, unaware of callers' deception, analyzed each 911 call using 86 behavioral cues. Using the random forest model with k-fold cross-validation and repeated sampling, the study achieved an accuracy rate of 68.2% for all 911 calls, with sensitivity and specificity at 68.7% and 67.7%, respectively. For homicide reports, accuracy was higher at 71.2%, with a sensitivity of 77.3% but slightly lower specificity at 65.0%. In contrast, accuracy decreased to 61.4% for missing person reports, with a sensitivity of 49.1% and notably higher specificity at 73.6%. Beyond accuracy, key cues distinguishing FACs from TRCs were identified and included cues like "Blames others," "Is self-dramatizing," and "Is uncertain and insecure." pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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