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Paper Index on ACL Anthology

A Bounded Coordination-Support Capability for Multi-Party Settings: Task-State Monitoring in Firefighter Incident Command A Dataset of Latin Etymologies Extracted from Wiktionary An Efficient Approach for Answering Not Readily Attainable Questions for RAG-based Applications Automated German Alt Text Generation for News Charts Call Support Copilot: A Reproducible Multimodal System for Speech Emotion Recognition, Intent Understanding, and Agent Assistance Can Large Language Models Replace Statistical Software? Code-Switching Detection in Multilingual Child Speech with SwissBERT Concept Extraction and Webb’s Depth of Knowledge: Comparing LLM Question Generation Pipelines for Educational Assessment Data Augmentation for Historical NER: A Systematic Comparison of Lexical and LLM-based Approaches Enhancing Retrieval via Cognitively Motivated Document Expansion Extending the Contact Hypothesis: Cross-Linguistic Evaluation of Religion and Nationality Bias When Prompting LLMs in German and Icelandic Extracting Article-Level Legal Dependencies from Swiss Federal Law using LLMs How Good is AI on Swiss Voting Booklets? A Multilingual OCR and Alignment Benchmark Optimizing Large Language Models for Robust Domain-Specific Text-to-SQL: From Prompting to Preference Alignment Proceedings of the 11th Edition of the Swiss Text Analytics Conference Reinforcement Learning for Latent-Space Thinking in LLMs RUMLEM: A Dictionary-Based Lemmatizer for Romansh Skill Extraction from Resumes and Job Offers across Six Languages Text vs. Phoneme Intermediates for Low-Resource Swiss German The Same Email, Signed Differently: Testing Negotiation Bias and Recommendation Stability in LLMs Which Skills Debate Reaches the Public? Comparing Scientific Literature and Media Coverage of AI and LLM Skill Impacts (2022–2025) Controlling Language and Style of Multi-lingual Generative Language Models with Control Vectors Hybrid Human-LLM Corpus Construction and LLM Evaluation for the Caused-Motion Construction Implicit and Indirect: Detecting Face-threatening and Paired Actions in Asynchronous Online Conversations Northern European Journal of Language Technology, Volume 11 A modular architecture for creating multimodal embodied agents with an episodic Knowledge Graph as an explainable and controllable long-term memory A Neural Approach to Discourse Relation Signal Detection An Analysis of Japanese Sentence-final Particle Yone: Compare Yone and Ne in Response Attribution and the discourse structure of reports Automatic Detection of the Bulgarian Evidential Renarrative
Please, Please, Just Tell Me: The Linguistic Features of ...
2026-04-20 · via Paper Index on ACL Anthology

Abstract

Prior research undertaken for the purpose of identifying deceptive language has focused on deception as it is used for nefarious ends, such as purposeful lying. However, despite the intent to mislead, not all examples of deception are carried out for malevolent ends. In this study, we describe the linguistic features of humorous deception. Specifically, we analyzed the linguistic features of 753 news stories, 1/3 of which were truthful and 2/3 of which we categorized as examples of humorous deception. The news stories we analyzed occurred naturally as part of a segment named Bluff the Listener on the popular American radio quiz show Wait, Wait...Don’t Tell Me!. Using a combination of supervised learning and predictive modeling, we identified 11 linguistic features accounting for approximately 18% of the variance between humorous deception and truthful news stories. These linguistic features suggested the deceptive news stories were more confident and descriptive but also less cohesive when compared to the truthful new stories. We suggest these findings reflect the dual communicative goal of this unique type of discourse to simultaneously deceive and be humorous.

Anthology ID:
2020.dnd-11.1
Volume:
Dialogue & Discourse Volume 11
Month:
December
Year:
2020
Address:
Chicago, Illinois, USA
Editors:
Massimo Poesio, Manfred Stede, Amanda Stent, Jonathan Ginzburg, Vera Demberg, Amir Zeldes
Venue:
DND
SIG:
SIGDIAL
Publisher:
University of Illinois Chicago
Note:
Pages:
128–149
Language:
URL:
https://aclanthology.org/2020.dnd-11.1/
DOI:
10.5210/dad.2020.205
Bibkey:
Cite (ACL):
Stephen Skalicky, Nicholas Duran, and Scott A Crossley. 2020. Please, Please, Just Tell Me: The Linguistic Features of Humorous Deception. Dialogue & Discourse, 11:128–149.
Cite (Informal):
Please, Please, Just Tell Me: The Linguistic Features of Humorous Deception (Skalicky et al., DND 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.dnd-11.1.pdf