# Argotario: Computational Argumentation Meets Serious Games

arXiv: https://arxiv.org/abs/1707.06002 · HTML: https://mlsanity.com/paper/1707.06002/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 30 of 30)

- [SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images](https://mlsanity.com/paper/2105.09284/) (2021)
- [Detecting Propaganda Techniques in Memes](https://mlsanity.com/paper/2109.08013/) (2021)
- [Multitask Instruction-based Prompting for Fallacy Recognition](https://mlsanity.com/paper/2301.09992/) (2023)
- [Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles](https://mlsanity.com/paper/2402.17478/) (2024)
- [ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text](https://mlsanity.com/paper/2311.03179/) (2023)
- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- [Interpretable Propaganda Detection in News Articles](https://mlsanity.com/paper/2108.12802/) (2021)
- [Missci: Reconstructing Fallacies in Misrepresented Science](https://mlsanity.com/paper/2406.03181/) (2024)
- [Are LLMs Good Zero-Shot Fallacy Classifiers?](https://mlsanity.com/paper/2410.15050/) (2024)
- [The Spread of Propaganda by Coordinated Communities on Social Media](https://mlsanity.com/paper/2109.13046/) (2021)
- [MemeIntel: Explainable Detection of Propagandistic and Hateful Memes](https://mlsanity.com/paper/2502.16612/) (2025)
- [CoCoLoFa: A Dataset of News Comments with Common Logical Fallacies Written by LLM-Assisted Crowds](https://mlsanity.com/paper/2410.03457/) (2024)
- [Fine-grained Fallacy Detection with Human Label Variation](https://mlsanity.com/paper/2502.13853/) (2025)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models](https://mlsanity.com/paper/2305.03336/) (2023)
- [Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition](https://mlsanity.com/paper/2311.09552/) (2023)
- [ArMeme: Propagandistic Content in Arabic Memes](https://mlsanity.com/paper/2406.03916/) (2024)
- [Towards a Perspectivist Turn in Argument Quality Assessment](https://mlsanity.com/paper/2502.14501/) (2025)
- [MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification](https://mlsanity.com/paper/2311.09761/) (2023)
- [Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling](https://mlsanity.com/paper/2406.12402/) (2024)
- [Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection](https://mlsanity.com/paper/2510.09695/) (2025)
- [Detection of Propaganda Techniques in Visuo-Lingual Metaphor in Memes](https://mlsanity.com/paper/2205.02937/) (2022)
- [Modeling Appropriate Language in Argumentation](https://mlsanity.com/paper/2305.14935/) (2023)
- [Joint Effects of Argumentation Theory, Audio Modality and Data Enrichment on LLM-Based Fallacy Classification](https://mlsanity.com/paper/2509.11127/) (2025)
- [Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation](https://mlsanity.com/paper/2307.15341/) (2023)
- [How Ethos and Pathos Appeals Resonate in Reader Interpretations of Social Media Messages](https://mlsanity.com/paper/2607.00873/) (2026)
- [A Corpus of Persuasion Techniques in Slavic Languages](https://mlsanity.com/paper/2607.10715/) (2026)
- [Retrieving Relations, Detecting Fallacies: A RAG Approach to Political Debate Analysis](https://mlsanity.com/paper/2608.27471/) (2026)
- [Fallacy Benchmarks Measure Scheme Recognition, Not Fallacy Detection](https://mlsanity.com/paper/2609.18644/) (2026)
- [Reasoning for Propaganda Detection via Label-Guided CoT Optimisation](https://mlsanity.com/paper/2610.17271/) (2026)

## Abstract (arXiv)

An important skill in critical thinking and argumentation is the ability to spot and recognize fallacies. Fallacious arguments, omnipresent in argumentative discourse, can be deceptive, manipulative, or simply leading to `wrong moves' in a discussion. Despite their importance, argumentation scholars and NLP researchers with focus on argumentation quality have not yet investigated fallacies empirically. The nonexistence of resources dealing with fallacious argumentation calls for scalable approaches to data acquisition and annotation, for which the serious games methodology offers an appealing, yet unexplored, alternative. We present Argotario, a serious game that deals with fallacies in everyday argumentation. Argotario is a multilingual, open-source, platform-independent application with strong educational aspects, accessible at www.argotario.net.