Detecting Deception through Linguistic Analysis

Burgoon, Judee K.; Blair, J. Pete; Qin, Tiantian; Nunamaker, Jay F.

doi:10.1007/3-540-44853-5_7

Cited by 136 publications

(131 citation statements)

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“…Deception Detection researchers also widely acknowledge a variation in linguistic cues as predictors across situations (Ali and Levine, 2008), across genres of communication, communicators (Burgoon et al, 2003) and cultures (Rubin, 2014). The main lesson we are learning is that the contexts in which deceptive communications occurs matter greatly.…”

Section: Methodological Solutions: Deception Detection With Linguistimentioning

confidence: 99%

Deception Detection and Rumor Debunking for Social Media

Rubin¹

2016

The SAGE Handbook of Social Media Research Methods

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The main premise of this chapter is that the time is ripe for more extensive research and development of social media tools that filter out intentionally deceptive information such as deceptive memes, rumors and hoaxes, fake news or other fake posts, tweets and fraudulent profiles. Social media users' awareness of intentional manipulation of online content appears to be relatively low, while the reliance on unverified information (often obtained from strangers) is at an all-time high. I argue there is need for content verification, systematic fact-checking and filtering of social media streams. This literature survey provides a background for understanding current automated deception detection research, rumor debunking, and broader content verification methodologies, suggests a path towards hybrid technologies, and explains why the development and adoption of such tools might still be a significant challenge. Biographical DetailsVictoria L. Rubin is an Associate Professor at the Faculty of Information and Media Studies and the Director of the Language and Information Technologies Research Lab (LiT.RL) at the University of Western Ontario. She specializes in information retrieval and natural language processing techniques that enable analyses of texts to identify, extract, and organize structured knowledge. She studies complex human information behaviors that are, at least partly, expressed through language such as deception, uncertainty, credibility, and emotions. Her research on Deception Detection has been published in recent core workshops on the topic and prominent information science conferences, as well as the

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Section: Methodological Solutions: Deception Detection With Linguistimentioning

confidence: 99%

Deception Detection and Rumor Debunking for Social Media

Rubin¹

2016

The SAGE Handbook of Social Media Research Methods

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“…Linguistic analysis techniques can prove useful for identifying relevant textual documents [13]. However, in complex situations such as fraud, it would need to be known in advance what specific language would be used to indicate that a crime has been committed.…”

Section: Linguistic Analysismentioning

confidence: 99%

A case-based reasoning method for locating evidence during digital forensic device triage

Horsman

Laing

Vickers

2014

Decision Support Systems

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The role of triage in digital forensics is disputed, with some practitioners questioning its reliability for identifying evidential data. Although successfully implemented in the field of medicine, triage has not established itself to the same degree in digital forensics. This article presents a novel approach to triage for digital forensics. Case-Based Reasoning Forensic Triager (CBR-FT) is a method for collecting and reusing past digital forensic investigation information in order to highlight likely evidential areas on a suspect operating system, thereby helping an investigator to decide where to search for evidence. The CBR-FT framework is discussed and the results of twenty test triage examinations are presented. CBR-FT has been shown to be a more effective method of triage when compared to a practitioner using a leading commercial application.

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“…Research into oral deception indicates that deceivers adopt linguistic strategies according to their degree of preparation (Anolli, Balconi & Ciceri, 2002;Burgoon, Blair, Qin & Nunamaker, 2003) and constantly adapt them to the attitude of their addressees (Anolli, Balconi & Ciceri, 2002). Thus, deceivers are able to choose the most effective approach to persuade or retreat as needed, which enables them to camouflage their deception within the interpersonal environment in which it occurs.…”

Section: Witness Statements As Textual Narrativesmentioning

confidence: 99%

Analysing Deception in Written Witness Statements

Picornell

2013

LESLI

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Written witness statements are a unique source for the study of high-stakes textual deception. To date, however, there is no distinction in the way that they and other forms of verbal deception have been analysed, with written statements treated as extensions of transcribed versions of oral reports. Given the highly context-dependent nature of cues, it makes sense to take the characteristics of the medium into account when analysing for deceptive language. This study examines the characteristic features of witness narratives and proposes a new approach to search for deception cues. Narratives are treated as a progression of episodes over time, and deception as a progression of acts over time. This allows for the profiling of linguistic bundles in sequence, revealing the statements' internal gradient, and deceivers' choice of deceptive linguistic strategy. Study results suggest that, at least in the context of written witness statements, the weighting of individual features as deception cues is not static but depends on their interaction with other cues, and that detecting deceivers' use of linguistic strategy is an effective vehicle for identifying deception.Keywords: detecting deception; witness statements; narratives; linguistic strategy; text analysis. Analysing Deception in Written Witness StatementsTo date, the information we have regarding behavioural cues to deception has been largely obtained from laboratory-based experiments dealing with low-consequence lies and psychological perspectives as to how liars should behave when lying. Their success is difficult to quantify as no one has yet identified a single cue, or set of cues, that is consistently identified with deception. DePaulo et al. (2003) suggest that behavioural cues to deception are normally faint unless liars are highly motivated to lie. They are thought to be more motivated to succeed with their deception when the consequences of being caught out are serious; being burdened by guilt at their transgression, or worry about implications to their reputation, or fear of imprisonment should the truth be revealed, results in more obvious behavioural cues which betray deceptive intentions. Such motivation is rarely found in the

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Detecting Deception through Linguistic Analysis

Cited by 136 publications

References 5 publications

Deception Detection and Rumor Debunking for Social Media

Deception Detection and Rumor Debunking for Social Media

A case-based reasoning method for locating evidence during digital forensic device triage

Analysing Deception in Written Witness Statements

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