| Challenge: | Deception occurs during everyday conversations, but this setting has received little attention from the research community. |
| Approach: | They propose to analyze multimodal deceptive dialogues in a box of lies game . they use facial and linguistic annotations to identify deceptives and truthful behaviors . |
| Outcome: | The proposed model outperforms both a random and a human baseline and achieves up to 69% accuracy in distinguishing deceptive and truthful behaviors. |
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Linguistic Cues to Deception and Perceived Deception in Interview Dialogues (N18-1)
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| Challenge: | a recent study examined deception detection in several domains, including fake reviews, mock crime scenes, and opinions about topics such as abortion or the death penalty. |
| Approach: | They analyze linguistic features in truthful and deceptive interview dialogues . they also examine interviewer perceptions of deception, identifying characteristics of deceptives . |
| Outcome: | The proposed model outperforms human classifications using linguistic features and individual traits. |
“Does it Matter When I Think You Are Lying?” Improving Deception Detection by Integrating Interlocutor’s Judgements in Conversations (2021.findings-acl)
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| Challenge: | Existing methods for deception detection are based on interrogator's perceptions of truth-bias . despite its frequent occurrences, human is not good at detecting deceptions despite inclination of truth bias . |
| Approach: | They propose a Judgmental-Enhanced Automatic Deception Detection Network that explicitly considers interrogator's perceived truths-deceptions with three types of speechlanguage features extracted during a conversation. |
| Outcome: | The proposed method outperforms the current state-of-the-art approach without conditioning on interrogator's judgements. |
To Tell The Truth: Language of Deception and Language Models (2024.naacl-long)
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| Challenge: | Existing evidence of people’s ability to discern truth from text-based false information is scarce. |
| Approach: | They propose to use a large language model to learn discernible cues from TV game show data to investigate whether textual cue is more likely to detect fraud . |
| Outcome: | The proposed model detects novel but accurate language cues in many cases where humans failed to detect deception. |
Hidden in Plain Sight: Evaluation of the Deception Detection Capabilities of LLMs in Multimodal Settings (2025.acl-long)
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| Challenge: | Detecting deception in an increasingly digital world is a critical and challenging task. |
| Approach: | They evaluate the performance of both open-source and proprietary LLMs on three datasets . they find that fine-tuned LLM achieve state-of-the-art performance on textual deception detection . |
| Outcome: | The proposed models achieve state-of-the-art on textual deception detection, whereas LMMs struggle to fully leverage multimodal cues. |
Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies (2020.tacl-1)
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| Challenge: | LieCatcher collects ratings of perceived deception using corpus of deceptive and truthful interviews . acoustic-prosodic and linguistic characteristics of language trusted and mistrusted are not reliable cues . |
| Approach: | They used a game framework to collect ratings of perceived deception using deceptive and truthful interviews to understand how perception aligns with reality. |
| Outcome: | The proposed framework detects deception using a corpus of deceptive and truthful interviews. |
Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection (2022.acl-srw)
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| Challenge: | Deception detection is a deliberate choice to mislead to gain some advantage or avoid some penalty. |
| Approach: | They propose to use inter-domain distance to identify suitable source domain for a given target domain to improve cross-domain deception classification and to better understand multi-modal deception detection. |
| Outcome: | The proposed methods will be able to detect deception in cross-domain, cross-lingual and multi-modal settings and will improve multi-modular deception classification. |
Construction and Analysis of a Multimodal Chat-talk Corpus for Dialog Systems Considering Interpersonal Closeness (2020.lrec-1)
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| Challenge: | a large-scale multimodal dialog corpus is needed to accelerate research on dialog systems that can handle social signals and verbal information. |
| Approach: | They construct a multimodal dialog corpus focusing on the relationship between speakers and 19 pairs of participants. |
| Outcome: | The proposed system is based on a multimodal dialog corpus of 19,303 utterances (10 hours) from 19 pairs of participants. |
BERTective: Language Models and Contextual Information for Deception Detection (2021.eacl-main)
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| Challenge: | Existing methods to classify texts as truthful or deceptive are limited by the context of the text being analyzed. |
| Approach: | They propose to use a corpus of Italian dialogues to classify texts as truthful or deceptive. |
| Outcome: | The proposed models show that not all contexts are equally useful to the task. |
Rhetorical Structure Approach for Online Deception Detection: A Survey (2022.lrec-1)
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| Challenge: | Existing studies on how people use language to inform and misinform are relevant. |
| Approach: | They analyze how discourse structure is applied to fake news detection on the web and social media. |
| Outcome: | The proposed framework is applied to fake news and fake reviews detection on the web and social media. |
Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games (2023.findings-acl)
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Bolin Lai, Hongxin Zhang, Miao Liu, Aryan Pariani, Fiona Ryan, Wenqi Jia, Shirley Anugrah Hayati, James Rehg, Diyi Yang
| Challenge: | Existing studies on persuasive behavior modeling focus on textual dialogues . a multimodal dataset is available for persuasion modeling . |
| Approach: | They propose a multimodal dataset for modeling persuasive behaviors using visual signals. |
| Outcome: | The proposed dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting and 26,647 utterance level annotations of persuasion strategy and game level annotation of deduction game outcomes. |