Gender Differences in Deception and Its Detection Under Varying Electronic Media Conditions

Tilley, Patti; George, Joey F.; Marett, Kent

doi:10.1109/hicss.2005.284

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…(Kaina et al, 2011) found that females are more easily detectable when lying than their male counterparts. (Tilley et al, 2005) reported that females are more successful in deception detection than male receivers.…”

Section: Related Workmentioning

confidence: 99%

Experiments in Open Domain Deception Detection

Pérez-Rosas¹,

Mihalcea²

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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The widespread use of deception in online sources has motivated the need for methods to automatically profile and identify deceivers. This work explores deception, gender and age detection in short texts using a machine learning approach. First, we collect a new open domain deception dataset also containing demographic data such as gender and age. Second, we extract feature sets including n-grams, shallow and deep syntactic features, semantic features, and syntactic complexity and readability metrics. Third, we build classifiers that aim to predict deception, gender, and age. Our findings show that while deception detection can be performed in short texts even in the absence of a predetermined domain, gender and age prediction in deceptive texts is a challenging task. We further explore the linguistic differences in deceptive content that relate to deceivers gender and age and find evidence that both age and gender play an important role in people's word choices when fabricating lies.

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Section: Related Workmentioning

confidence: 99%

Experiments in Open Domain Deception Detection

Pérez-Rosas¹,

Mihalcea²

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…Published by Cambridge University Press. As many studies suggest, deception is an act that depends on many factors such as personality (Fornaciari et al 2013;Levitan et al 2015), age (Sweeney and Ceci 2014), gender (Tilley et al 2005;Toma et al 2008;Fu et al 2008), or culture (Taylor et al 2014;Taylor et al 2017;Leal et al 2018). All these factors affect the way and the means one uses to deceive.…”

Section: Introductionmentioning

confidence: 99%

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

et al. 2021

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Automatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.

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“…The number of interpreting cues is fewer, because message recipients neither see nor hear the sender. The disconnect between recipients and senders precludes access to visual cues (facial expressions, gestures) and vocal cues (voice tones) that may be useful for interpreting message content and for understanding the intent of message senders (e.g., Tilley et al 2005). The result may be diminished decision-making quality (e.g., Kahai and Cooper 2003).…”

Section: Introductionmentioning

confidence: 97%

Features of Computer-Mediated, Text-Based Messages that Support Automatable, Linguistics-Based Indicators for Deception Detection

Lee

Welker

Odom

2009

Journal of Information Systems

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Researchers are assessing the viability of automating deception detection in TAC (text-based, asynchronous, computer-mediated) messages. Viability is conditional on whether deceivers incorporate different features in their TAC messages than truth tellers do and on whether these differentiating features support deception indicators suitable for automation. We propose that deceivers incorporate different features in their messages to realize impression-management goals. They avoid message features people associate with lying (defensive-targeted features) and include message features people associate with truth telling (promotive-targeted features). In a series of three studies, we assessed features that may be part of deceivers' impressionmanagement strategies and then assessed whether deceivers' messages contain different levels of these features than truth tellers' messages. The results show that deceivers construct different messages than truth tellers do and that the differences relate to promotive-targeted features. These findings suggest that researchers look to promotive goals of deceivers for automatable deception indicators in TAC communication.

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Gender Differences in Deception and Its Detection Under Varying Electronic Media Conditions

Cited by 11 publications

References 37 publications

Experiments in Open Domain Deception Detection

Experiments in Open Domain Deception Detection

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

Features of Computer-Mediated, Text-Based Messages that Support Automatable, Linguistics-Based Indicators for Deception Detection

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