UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection

Verbal deception has been studied in psychology, forensics, and computational linguistics for a variety of reasons, like understanding behaviour patterns, identifying false testimonies, and detecting deception in online communication. Varying motivations across research fields lead to differences in the domain choices to study and in the conceptualization of deception, making it hard to compare models and build robust deception detection systems for a given language. With this paper, we improve this situation by surveying available English deception datasets which include domains like social media reviews, court testimonials, opinion statements on specific topics, and deceptive dialogues from online strategy games. We consolidate these datasets into a single unified corpus. Based on this resource, we conduct a correlation analysis of linguistic cues of deception across datasets to understand the differences and perform cross-corpus modeling experiments which show that a cross-domain generalization is challenging to achieve. The unified deception corpus (UNIDECOR) can be obtained from https://www.ims.uni-stuttgart.de/data/unidecor

Decoding social media speak: developing a speech act theory research agenda

Purpose – Drawing on the theoretical domain of speech act theory (SAT) and a discussion of its suitability for setting the agenda for social media research, this study aims to explore a range of research directions that are both relevant and conceptually robust, to stimulate the advancement of knowledge and understanding of online verbatim data. Design/methodology/approach – Examining previously published cross-disciplinary research, the study identifies how recent conceptual and empirical advances in SAT may further guide the development of text analytics in a social media context. Findings – Decoding content and function word use in customers’ social media communication can enhance the efficiency of determining potential impacts of customer reviews, sentiment strength, the quality of contributions in social media, customers’ socialization perceptions in online communities and deceptive messages. Originality/value – Considering the variety of managerial demand, increasing and diverging social media formats, expanding archives, rapid development of software tools and fast-paced market changes, this study provides an urgently needed, theory-driven, coherent research agenda to guide the conceptual development of text analytics in a social media context

Speech with pauses sounds deceptive to listeners with and without hearing impairment

Purpose: Communication is as much persuasion as it is the transfer of information. This creates a tension between the interests of the speaker and those of the listener as dishonest speakers naturally attempt to hide deceptive speech, and listeners are faced with the challenge of sorting truths from lies. Hearing impaired listeners in particular may have differing levels of access to the acoustical cues that give away deceptive speech. A greater tendency towards speech pauses has been hypothesised to result from the cognitive demands of lying convincingly. Higher vocal pitch has also been hypothesised to mark the increased anxiety of a dishonest speaker.// Method: listeners with or without hearing impairments heard short utterances from natural conversations some of which had been digitally manipulated to contain either increased pausing or raised vocal pitch. Listeners were asked to guess whether each statement was a lie in a two alternative forced choice task. Participants were also asked explicitly which cues they believed had influenced their decisions.// Results: Statements were more likely to be perceived as a lie when they contained pauses, but not when vocal pitch was raised. This pattern held regardless of hearing ability. In contrast, both groups of listeners self-reported using vocal pitch cues to identify deceptive statements, though at lower rates than pauses.// Conclusions: Listeners may have only partial awareness of the cues that influence their impression of dishonesty. Hearing impaired listeners may place greater weight on acoustical cues according to the differing degrees of access provided by hearing aids./

Pauses in Deceptive Speech

We use a corpus of spontaneous interview speech to investigate the relationship between the distributional and prosodic characteristics of silent and filled pauses and the intent of an interviewee to deceive an interviewer. Our data suggest that the use of pauses correlates more with truthful than with deceptive speech, and that prosodic features extracted from filled pauses themselves as well as features describing contextual prosodic information in the vicinity of filled pauses may facilitate the detection of deceit in speech

Deception in Spoken Dialogue: Classification and Individual Differences

Automatic deception detection is an important problem with far-reaching implications in many areas, including law enforcement, military and intelligence agencies, social services, and politics. Despite extensive efforts to develop automated deception detection technologies, there have been few objective successes. This is likely due to the many challenges involved, including the lack of large, cleanly recorded corpora; the difficulty of acquiring ground truth labels; and major differences in incentives for lying in the laboratory vs. lying in real life. Another well-recognized issue is that there are individual and cultural differences in deception production and detection, although little has been done to identify them. Human performance at deception detection is at the level of chance, making it an uncommon problem where machines can potentially outperform humans. This thesis addresses these challenges associated with research of deceptive speech. We created the Columbia X-Cultural Deception (CXD) Corpus, a large-scale collection of deceptive and non-deceptive dialogues between native speakers of Standard American English and Mandarin Chinese. This corpus enabled a comprehensive study of deceptive speech on a large scale. In the first part of the thesis, we introduce the CXD corpus and present an empirical analysis of acoustic-prosodic and linguistic cues to deception. We also describe machine learning classification experiments to automatically identify deceptive speech using those features. Our best classifier achieves classification accuracy of almost 70%, well above human performance. The second part of this thesis addresses individual differences in deceptive speech. We present a comprehensive analysis of individual differences in verbal cues to deception, and several methods for leveraging these speaker differences to improve automatic deception classification. We identify many differences in cues to deception across gender, native language, and personality. Our comparison of approaches for leveraging these differences shows that speaker-dependent features that capture a speaker's deviation from their natural speaking style can improve deception classification performance. We also develop neural network models that accurately model speaker-specific patterns of deceptive speech. The contributions of this work add substantially to our scientific understanding of deceptive speech, and have practical implications for human practitioners and automatic deception detection

Forensic transcript analysis: A forensic linguistic examination of a 2015 criminal case in the United States

Forensic linguistics is an emerging field of research that applies linguistics to analyze language and its use in a legal setting, including criminal, civil, and family court proceedings. Police interviews are a critical source of evidence in law enforcement investigations, and the quality of the interview process is essential in analyzing police interview transcripts. The detection of deception is a significant challenge in various contexts, including law enforcement, politics, business, and personal relationships. Language patterns can signal deception and indicate underlying cognitive and emotional processes. Therefore, in this study, special attention is given to the use of language, both verbal and non-verbal cues, to gauge the veracity of an individual, as well as the application of police interview techniques and discourse analysis. The study aims to determine the reliability of the suspect's statements during a police interview and explore the effectiveness of different police interview techniques and their ethical implications. The analysis will draw on existing research in the field of forensic linguistics and aims to provide an accurate understanding of police interviews to inform legal decisions. The thesis analyzes the reliability of linguistic information gathered through interviews in a 2015 criminal case in the United States, using a forensic linguistic perspective. The thesis explores relevant literature on police interviews, deception, misrepresentations, interview techniques, and reliability discourse analysis. The research questions focus on the reliability of police interviews, in particular on the spoken word, misrepresentations within police interviews. The second research question focuses on the use of police techniques and how they affect the reliability of interviews. The findings suggest a nuanced impression of the reliability of the statements made within the transcripts

Exploring Iranian EFL Learners’ (In)sincerity in Compliments through Prosodic Features

Prosody is a fundamental aspect of speech communication through which (un)truthfulness and (in)sincerity of speech can be identified. The focus of the study is on the prosodic features of (in)sincere compliments among EFL learners. Twenty male and female EFL learners were selected through Oxford Quick Placement Test. The participants did role-plays based on situations on compliment topics and their voices were recorded in a recording studio. The produced compliments were transferred to Praat software for acoustic analysis. Also, two native speakers (one male and one female) were requested to read the produced compliments both in a sincere and insincere manner. Their voices were transferred to Praat software for acoustic analysis to establish the baseline of the study. The prosodic features of the participants’ voices were compared with those of native speakers to determine the (in)sincerity of the compliments on a 5-point scale. Results showed that sincere compliments are produced with a higher pitch. Concerning the gender of the participants, males were sincerer than females. Regarding the proficiency level of the participants, there was no significant prosodic feature in determining the sincerity of their compliments. Both intermediate and advanced groups were similar to native speakers in giving sincere compliments. The results of the study open up new horizons for the importance of vocal cues in evaluating sincerity in speech acts