It Takes Two to Lie: One to Lie, and One to Listen

Peskov, Denis; Cheng, Benny N.; Elgohary, Ahmed; Barrow, Joe; Danescu-Niculescu-Mizil, Cristian; Boyd-Graber, Jordan

doi:10.18653/v1/2020.acl-main.353

Cited by 14 publications

(11 citation statements)

References 44 publications

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“…Our results concerning BERT's usefulness with context are different from those obtained by Peskov et al (2020), who work on Diplomacy board-game deception data. Their study associated BERT to LSTM-based contextual models, and they did not find a BERT contribution in their model's performance.…”

Section: Discussioncontrasting

confidence: 99%

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BERTective: Language Models and Contextual Information for Deception Detection

Fornaciari

Bianchi

Poesio

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts' preceding context is not considered. This is a severe limitation, as any communication takes place in context, not in a vacuum, and context can help to detect deception. We study a corpus of Italian dialogues containing deceptive statements and implement deep neural models that incorporate various linguistic contexts. We establish a new state-of-theart identifying deception and find that not all context is equally useful to the task. Only the texts closest to the target, if from the same speaker (rather than questions by an interlocutor), boost performance. We also find that the semantic information in language models such as BERT contributes to the performance. However, BERT alone does not capture the implicit knowledge of deception cues: its contribution is conditional on the concurrent use of attention to learn cues from BERT's representations.

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Section: Discussioncontrasting

confidence: 99%

“…From a methodological point of view, our study is similar to that by Peskov et al (2020). They collect data from an online negotiation game, where the participants' success depends on their ability to lie.…”

Section: Related Workmentioning

confidence: 99%

BERTective: Language Models and Contextual Information for Deception Detection

Fornaciari

Bianchi

Poesio

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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“…• Diplomacy (DM) (Peskov et al, 2020): conversation logs collected from Diplomacy, an online text-based board game. : true and false courtroom testimonies collected from InnocenceProject.org 2 .…”

Section: Datasetsmentioning

confidence: 99%

“…and the min. dominance scores, their differences, the number of strong categories Note that liars tend to wrap their lies with true information in an effort to be convincing (Peskov et al, 2020), i.e., lies are diluted by truth.…”

Section: Analysis By Datasetmentioning

confidence: 99%

“…Many NLP studies have recently collected lie detection datasets and detected lies using computational models (Hirschberg et al, 2005;Pérez-Rosas et al, 2014;Peskov et al, 2020); most of these ignore traditional lie detection methods and findings, and have no follow-up studies, making it difficult to know which datasets are suitable for model training.…”

Section: Introductionmentioning

confidence: 99%

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Lying Through One’s Teeth: A Study on Verbal Leakage Cues

Yeh¹,

Ku²

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Although many studies use the LIWC lexicon to show the existence of verbal leakage cues in lie detection datasets, none mention how verbal leakage cues are influenced by means of data collection, or the impact thereof on the performance of models. In this paper, we study verbal leakage cues to understand the effect of the data construction method on their significance, and examine the relationship between such cues and models' validity. The LIWC word-category dominance scores of seven lie detection datasets are used to show that audio statements and lie-based annotations indicate a greater number of strong verbal leakage cue categories. Moreover, we evaluate the validity of state-of-the-art lie detection models with cross-and in-dataset testing. Results show that in both types of testing, models trained on a dataset with more strong verbal leakage cue categories-as opposed to only a greater number of strong cues-yield superior results, suggesting that verbal leakage cues are a key factor for selecting lie detection datasets.

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Trust development in online competitive game environments: a network analysis approach

Fehlhaber,

EL-Awad

2024

Appl Netw Sci

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Trust seems to become established even in scenarios where the prerequisites for trust are complicated by conditions that evoke scepticism. Nonetheless, trust emerges, a phenomenon that is to be comprehended and examined in the present experimental inquiry. In order to comprehensively capture the process, a competitive online game environment was used to document the development of trust networks, directionality, and strength using network analysis. Despite the conditions conducive to distrust in this game setting, acts of trust were exhibited.Robust trust bonds persisting over the course of gameplay appear to manifest mostly dyadic or triadic, with participant embeddedness within the network and homophily in terms of general trustfulness towards strangers being conducive factors for trust bonding and game survivability. This study hence contributes to the overall understanding of online trust development and offers several further research opportunities in a mostly unexplored field.

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It Takes Two to Lie: One to Lie, and One to Listen

Cited by 14 publications

References 44 publications

BERTective: Language Models and Contextual Information for Deception Detection

BERTective: Language Models and Contextual Information for Deception Detection

Lying Through One’s Teeth: A Study on Verbal Leakage Cues

Trust development in online competitive game environments: a network analysis approach

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