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Using Named Entities for Computer‐Automated Verbal Deception Detection
Author(s) -
Kleinberg Bennett,
Mozes Maximilian,
Arntz Arnoud,
Verschuere Bruno
Publication year - 2018
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.13645
Subject(s) - deception , discriminative model , lexicon , natural language processing , computer science , sentence , named entity recognition , artificial intelligence , information retrieval , psychology , social psychology , task (project management) , management , economics
There is an increasing demand for automated verbal deception detection systems. We propose named entity recognition ( NER ; i.e., the automatic identification and extraction of information from text) to model three established theoretical principles: (i) truth tellers provide accounts that are richer in detail, (ii) contain more contextual references (specific persons, locations, and times), and (iii) deceivers tend to withhold potentially checkable information. We test whether NER captures these theoretical concepts and can automatically identify truthful versus deceptive hotel reviews. We extracted the proportion of named entities with two NER tools (spaCy and Stanford's NER ) and compared the discriminative ability to a lexicon word count approach ( LIWC ) and a measure of sentence specificity (speciteller). Named entities discriminated truthful from deceptive hotel reviews above chance level, and outperformed the lexicon approach and sentence specificity. This investigation suggests that named entities may be a useful addition to existing automated verbal deception detection approaches.