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arxiv: 2606.26698 · v1 · pith:5J7EM6JSnew · submitted 2026-06-25 · 💻 cs.CL · cs.AI

Beyond Logical Forms: LLM-Extracted Patterns for Fallacy Classification

Eleni Papadopulos , Firoj Alam , Giovanni Da San Martino This is my paper

Pith reviewed 2026-06-26 05:13 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords fallacy classificationlogical fallaciesLLM pattern extractionzero-shot learninginformation disorderpattern inductionlinguistic cues
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The pith

LLM-extracted patterns that merge logical structures with linguistic cues improve fallacy classification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates whether combining abstract logical structures with context-level linguistic cues helps classify logical fallacies more accurately. It develops a framework that uses LLMs to inductively extract such patterns from fallacious examples and their explanations. This yields statistically significant gains over zero-shot baselines and outperforms other methods while generalizing across datasets. A reader would care because fallacies often appear in nuanced forms that fuel information disorder and resist simple automated detection. The work positions data-driven pattern extraction as a practical route to building useful logical representations.

Core claim

The framework inductively extracts patterns from fallacious examples and their explanations using LLMs, and these patterns prove beneficial for fallacy classification across different LLMs and zero- and one-shot configurations, outperforming baselines with statistical significance and generalizing across datasets.

What carries the argument

The inductive pattern extraction framework using LLMs that generates logical representations by merging abstract structures with linguistic cues.

If this is right

  • Statistically significant improvements over zero-shot baselines for fallacy classification.
  • Outperformance of competing approaches in the evaluated settings.
  • Validated generalization through cross-dataset experiments.
  • Data-driven pattern extraction serves as an effective method for generating logical representations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same extraction approach could be tested on other reasoning or argumentation tasks beyond fallacy detection.
  • The patterns might increase the interpretability of automated content moderation systems.
  • Similar methods could be applied to smaller language models to check whether the gains hold without large-scale LLMs.

Load-bearing premise

The LLM-generated patterns are faithful to the underlying logical defects rather than capturing spurious correlations in the training explanations or prompt phrasing.

What would settle it

No statistically significant improvement when adding the extracted patterns to baselines, or clear failure to generalize in cross-dataset tests, would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.26698 by Eleni Papadopulos, Firoj Alam, Giovanni Da San Martino.

Figure 1
Figure 1. Figure 1: Group-wise F1 score for each model, relative to the PATTERN MATCHING prompt setting. Furthermore, matching patterns allows us to see that some instances can be deemed as fitting from a structural point of view, thus partially explain￾ing the inherent difficulty of the classification task. While providing guidance through syntactic and logical structure proves beneficial for fallacy detec￾tion, this approac… view at source ↗
read the original abstract

In today's fast-paced information era, logical fallacies, defined as defective patterns of reasoning, inevitably contribute to the growth of information disorder. However, often fallacies appear in nuanced forms that complicate automated classification. In this study, we investigate whether merging abstract logical structures with context-level linguistic cues proves beneficial for fallacy classification, developing a framework that inductively extracts such patterns from fallacious examples and their explanations using Large Language Models (LLMs). We evaluate the impact of these patterns across different LLMs and experimental zero- and one-shot configurations, showing statistically significant improvements over zero-shot baselines and outperforming competing approaches. Cross-dataset experiments validate generalization, establishing data-driven pattern extraction as an effective method for generating logical representations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that inductively extracting patterns from fallacious examples and their human-written explanations via LLMs, then merging abstract logical structures with context-level linguistic cues, yields statistically significant improvements in fallacy classification over zero-shot baselines, outperforms competing methods, and generalizes across datasets.

Significance. If the extracted patterns prove to be faithful logical representations rather than artifacts, the framework could advance interpretable fallacy detection by providing a data-driven way to generate hybrid logical-linguistic features. The cross-dataset validation and multi-LLM evaluation are strengths that would support broader applicability if the core assumption holds.

major comments (2)
  1. [Methods / Experimental Setup] The central claim that the patterns constitute genuine logical representations (abstract structures + linguistic cues) requires evidence that performance gains arise from the logical defects rather than surface correlations in the human explanations or prompt templates. The reported zero-shot and cross-dataset results do not isolate this, as baselines lack the explanation-conditioned pattern extraction step.
  2. [Results / Ablation Studies] Without an ablation that generates patterns from fallacious examples alone (omitting explanations) and compares against the full condition, it remains possible that gains reflect explanation phrasing artifacts rather than the intended logical structures; this directly affects the interpretation of statistical significance claims.
minor comments (2)
  1. [Results] Clarify the exact statistical tests and correction procedures used for the reported significance levels, including sample sizes per dataset split.
  2. [Appendix] Provide the full prompt templates used for pattern extraction in an appendix to allow replication of the inductive step.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the need for stronger isolation of the logical components in our pattern extraction framework. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: [Methods / Experimental Setup] The central claim that the patterns constitute genuine logical representations (abstract structures + linguistic cues) requires evidence that performance gains arise from the logical defects rather than surface correlations in the human explanations or prompt templates. The reported zero-shot and cross-dataset results do not isolate this, as baselines lack the explanation-conditioned pattern extraction step.

    Authors: We agree that the current experimental design does not fully isolate whether gains derive specifically from the logical structures versus phrasing artifacts in the human explanations. While the cross-dataset generalization and multi-LLM consistency provide indirect support for transferable logical patterns, these do not directly rule out explanation-conditioned effects. To address this, we will add a controlled ablation in the revised manuscript that compares patterns extracted from examples alone against the full (example + explanation) condition. revision: yes

  2. Referee: [Results / Ablation Studies] Without an ablation that generates patterns from fallacious examples alone (omitting explanations) and compares against the full condition, it remains possible that gains reflect explanation phrasing artifacts rather than the intended logical structures; this directly affects the interpretation of statistical significance claims.

    Authors: This observation is correct. The manuscript currently lacks the requested ablation, which leaves the interpretation of the statistical significance open to the alternative explanation of explanation artifacts. We will perform and report this ablation study in the revision, allowing direct comparison of performance when explanations are omitted from the pattern extraction step. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical evaluation against external baselines

full rationale

The paper describes an empirical pipeline that uses LLMs to inductively extract patterns from examples plus explanations, then measures classification accuracy in zero-/one-shot settings and cross-dataset generalization. No equations, fitted parameters, or self-citations are shown that reduce the reported improvements to a quantity defined inside the paper itself. All performance claims rest on comparisons to external baselines rather than internal self-definition or renaming.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that LLM outputs can be treated as reliable logical representations without independent verification of their logical fidelity.

axioms (1)
  • domain assumption LLM-generated patterns faithfully capture logical defects rather than surface correlations in the input explanations.
    Invoked when the paper treats the extracted patterns as logical representations that improve classification.

pith-pipeline@v0.9.1-grok · 5647 in / 1185 out tokens · 17047 ms · 2026-06-26T05:13:28.275156+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

300 extracted references · 144 canonical work pages

  1. [1]

    arXiv preprint arXiv:2303.08774 , year=

    Pith/arXiv arXiv

  2. [2]

    Dubey, Abhimanyu and Jauhri, Abhinav and Pandey, Abhinav and Kadian, Abhishek and Al-Dahle, Ahmad and Letman, Aiesha and Mathur, Akhil and Schelten, Alan and Yang, Amy and Fan, Angela and others , journal=. The

  3. [3]

    2412.19437 , archivePrefix =

    arXiv preprint arXiv:2412.19437 , year =. 2412.19437 , archivePrefix =

    Pith/arXiv arXiv

  4. [4]

    Introduction to Logic , year =

    Irving Marmer Copi and Carl Cohen and Kenneth McMahon , publisher =. Introduction to Logic , year =

  5. [5]

    1999 , publisher=

    Logic from A to Z: The Routledge Encyclopedia of Philosophy Glossary of Logical and Mathematical Terms , author=. 1999 , publisher=

    1999

  6. [6]

    1970 , publisher=

    Fallacies , author=. 1970 , publisher=

    1970

  7. [7]

    Anthony Blair , editor =

    Ralph Henry Johnson and J. Anthony Blair , editor =. Logical Self-Defense , year =

  8. [8]

    Edward Damer , editor =

    T. Edward Damer , editor =. Attacking Faulty Reasoning: A Practical Guide to Fallacy-Free Arguments , year =

  9. [9]

    #$ denote the maximum number of permitted repair steps. If the initial Lean4 code fails to compile (state F), the framework activates an execution-feedback loop while 𝑖𝑡𝑒𝑟≤𝑖𝑡𝑒𝑟

    Jin, Zhijing and Lalwani, Abhinav and Vaidhya, Tejas and Shen, Xiaoyu and Ding, Yiwen and Lyu, Zhiheng and Sachan, Mrinmaya and Mihalcea, Rada and Sch. Logical Fallacy Detection. Findings of the Association for Computational Linguistics: EMNLP 2022. 2022. doi:10.18653/v1/2022.findings-emnlp.532

    work page doi:10.18653/v1/2022.findings-emnlp.532 2022

  10. [10]

    Multitask Instruction-based Prompting for Fallacy Recognition

    Alhindi, Tariq and Chakrabarty, Tuhin and Musi, Elena and Muresan, Smaranda. Multitask Instruction-based Prompting for Fallacy Recognition. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022. doi:10.18653/v1/2022.emnlp-main.560

    work page doi:10.18653/v1/2022.emnlp-main.560 2022

  11. [11]

    Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation

    Jeong, Jiwon and Jang, Hyeju and Park, Hogun. Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation. Findings of the Association for Computational Linguistics: NAACL 2025. 2025. doi:10.18653/v1/2025.findings-naacl.384

    work page doi:10.18653/v1/2025.findings-naacl.384 2025

  12. [12]

    A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning

    Hong, Ruixin and Zhang, Hongming and Pang, Xinyu and Yu, Dong and Zhang, Changshui. A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. doi...

    work page doi:10.18653/v1/2024.naacl-long.52 2024

  13. [13]

    Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition

    Alhindi, Tariq and Muresan, Smaranda and Nakov, Preslav. Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition. Findings of the Association for Computational Linguistics: ACL 2024. 2024. doi:10.18653/v1/2024.findings-acl.732

    work page doi:10.18653/v1/2024.findings-acl.732 2024

  14. [14]

    Are LLM s Good Zero-Shot Fallacy Classifiers?

    Pan, Fengjun and Wu, Xiaobao and Li, Zongrui and Luu, Anh Tuan. Are LLM s Good Zero-Shot Fallacy Classifiers?. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024. doi:10.18653/v1/2024.emnlp-main.794

    work page doi:10.18653/v1/2024.emnlp-main.794 2024

  15. [15]

    Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence,

    Case-Based Reasoning with Language Models for Classification of Logical Fallacies , author =. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence,. 2023 , month =. doi:10.24963/ijcai.2023/576 , url =

    work page doi:10.24963/ijcai.2023/576 2023

  16. [16]

    Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

    Reimers, Nils and Gurevych, Iryna. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. 2019

    2019

  17. [17]

    , title =

    Lim, Gionnieve and Perrault, Simon T. , title =. Companion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing , pages =. 2024 , isbn =. doi:10.1145/3678884.3681867 , abstract =

    work page doi:10.1145/3678884.3681867 2024

  18. [18]

    Boosting Logical Fallacy Reasoning in LLM s via Logical Structure Tree

    Lei, Yuanyuan and Huang, Ruihong. Boosting Logical Fallacy Reasoning in LLM s via Logical Structure Tree. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024. doi:10.18653/v1/2024.emnlp-main.730

    work page doi:10.18653/v1/2024.emnlp-main.730 2024

  19. [19]

    Proceedings of the AAAI Conference on Artificial Intelligence , author=

    Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-Based Test Oracles , volume=. Proceedings of the AAAI Conference on Artificial Intelligence , author=. 2026 , month=. doi:10.1609/aaai.v40i23.39021 , abstractNote=

    work page doi:10.1609/aaai.v40i23.39021 2026

  20. [20]

    Hempel and Paul Oppenheim

    Carl G. Hempel and Paul Oppenheim. Studies in the logic of explanation. Philosophy of science, vol. 15 (1948), pp. 135–175. , volume=. Journal of Symbolic Logic , author=. 1949 , pages=. doi:10.2307/2266531 , number=

    work page doi:10.2307/2266531 1948

  21. [21]

    Developing Fake News Immunity: Fallacies as Misinformation Triggers During the Pandemic , volume =

    Musi, Elena and Aloumpi, Myrto and Carmi, Elinor and Yates, Simeon and O’Halloran, Kay , year =. Developing Fake News Immunity: Fallacies as Misinformation Triggers During the Pandemic , volume =. Online Journal of Communication and Media Technologies , doi =

  22. [22]

    OpenAI o3 and o4-mini System Card , author=

  23. [23]

    Do Syntax Trees Help Pre-trained Transformers Extract Information?

    Sachan, Devendra and Zhang, Yuhao and Qi, Peng and Hamilton, William L. Do Syntax Trees Help Pre-trained Transformers Extract Information?. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021. doi:10.18653/v1/2021.eacl-main.228

    work page doi:10.18653/v1/2021.eacl-main.228 2021

  24. [24]

    Large Language Models for Logical Fallacy Detection

    Teo, Nicole and Huang, Donghao and Cambria, Erik and Wang, Zhaoxia. Large Language Models for Logical Fallacy Detection. Trends and Applications in Knowledge Discovery and Data Mining. 2025

    2025

  25. [25]

    Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding

    Li, Yanda and Wang, Dixuan and Liang, Jiaqing and Jiang, Guochao and He, Qianyu and Xiao, Yanghua and Yang, Deqing. Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding. Findings of the Association for Computational Linguistics: NAACL 2024. 2024. doi:10.18653/v1/2024.findings-naacl.192

    work page doi:10.18653/v1/2024.findings-naacl.192 2024

  26. [26]

    Language Models are Few-Shot Learners , url =

    Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel and Wu, Jeffrey and Winte...

    2020

  27. [27]

    Advances in neural information processing systems , volume=

    Chain-of-thought prompting elicits reasoning in large language models , author=. Advances in neural information processing systems , volume=

  28. [28]

    and Ying, Rex and Leskovec, Jure , biburl =

    Hamilton, William L. and Ying, Rex and Leskovec, Jure , biburl =. Representation Learning on Graphs: Methods and Applications. , url =. IEEE Data Eng. Bull. , keywords =

  29. [29]

    A Robustly Optimized BERT Pre-training Approach with Post-training

    Zhuang, Liu and Wayne, Lin and Ya, Shi and Jun, Zhao. A Robustly Optimized BERT Pre-training Approach with Post-training. Proceedings of the 20th Chinese National Conference on Computational Linguistics. 2021

    2021

  30. [30]

    Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling

    Robbani, Irfan and Reisert, Paul and Pothong, Surawat and Inoue, Naoya and Guerraoui, Cam \'e lia and Wang, Wenzhi and Naito, Shoichi and Choi, Jungmin and Inui, Kentaro. Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Pro...

    work page doi:10.18653/v1/2024.emnlp-main.1142 2024

  31. [31]

    Robust and explainable identification of logical fallacies in natural language arguments , journal =

    Zhivar Sourati and Vishnu Priya. Robust and explainable identification of logical fallacies in natural language arguments , journal =. 2023 , issn =. doi:https://doi.org/10.1016/j.knosys.2023.110418 , url =

    work page doi:10.1016/j.knosys.2023.110418 2023

  32. [32]

    , booktitle =

    Walton, Douglas N. , booktitle =. Informal logic : a pragmatic approach , year =

  33. [33]

    Argument-based Detection and Classification of Fallacies in Political Debates

    Goffredo, Pierpaolo and Chaves, Mariana and Villata, Serena and Cabrio, Elena. Argument-based Detection and Classification of Fallacies in Political Debates. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.684

    work page doi:10.18653/v1/2023.emnlp-main.684 2023

  34. [34]

    C o C o L o F a: A Dataset of News Comments with Common Logical Fallacies Written by LLM -Assisted Crowds

    Yeh, Min-Hsuan and Wan, Ruyuan and Huang, Ting-Hao Kenneth. C o C o L o F a: A Dataset of News Comments with Common Logical Fallacies Written by LLM -Assisted Crowds. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024. doi:10.18653/v1/2024.emnlp-main.39

    work page doi:10.18653/v1/2024.emnlp-main.39 2024

  35. [35]

    , isbn =

    Gensler, H.J. , isbn =. The to Z of Logic , url =. 2010 , bdsk-url-1 =

    2010

  36. [36]

    2025 , eprint=

    An Explainable Framework for Misinformation Identification via Critical Question Answering , author=. 2025 , eprint=

    2025

  37. [37]

    Detecting Argumentative Fallacies in the Wild: Problems and Limitations of Large Language Models

    Ruiz-Dolz, Ramon and Lawrence, John. Detecting Argumentative Fallacies in the Wild: Problems and Limitations of Large Language Models. Proceedings of the 10th Workshop on Argument Mining. 2023. doi:10.18653/v1/2023.argmining-1.1

    work page doi:10.18653/v1/2023.argmining-1.1 2023

  38. [38]

    When automated fact-checking meets argumentation: Unveiling fake news through argumentative evidence , url =

    Xiaoou Wang and Elena Cabrio and Serena Villata , doi =. When automated fact-checking meets argumentation: Unveiling fake news through argumentative evidence , url =. 2025 , bdsk-url-1 =. https://doi.org/10.1177/19462174251330980 , journal =

    work page doi:10.1177/19462174251330980 2025

  39. [39]

    Detecting Disinformation through Computational Argumentation Techniques and Large Language Models , booktitle =

    Guti. Detecting Disinformation through Computational Argumentation Techniques and Large Language Models , booktitle =. 2024 , series =

    2024

  40. [40]

    Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022). 2022

    2022

  41. [41]

    A Systematic Survey of Text Worlds as Embodied Natural Language Environments

    Jansen, Peter. A Systematic Survey of Text Worlds as Embodied Natural Language Environments. Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022). 2022. doi:10.18653/v1/2022.wordplay-1.1

    work page doi:10.18653/v1/2022.wordplay-1.1 2022

  42. [42]

    A Minimal Computational Improviser Based on Oral Thought

    Montfort, Nick and Bartlett Fernandez, Sebastian. A Minimal Computational Improviser Based on Oral Thought. Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022). 2022. doi:10.18653/v1/2022.wordplay-1.2

    work page doi:10.18653/v1/2022.wordplay-1.2 2022

  43. [43]

    Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code

    Volum, Ryan and Rao, Sudha and Xu, Michael and DesGarennes, Gabriel and Brockett, Chris and Van Durme, Benjamin and Deng, Olivia and Malhotra, Akanksha and Dolan, Bill. Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code. Proceedings of the 3rd Wordplay: When Language Meets Games Worksho...

    work page doi:10.18653/v1/2022.wordplay-1.3 2022

  44. [44]

    A Sequence Modelling Approach to Question Answering in Text-Based Games

    Furman, Gregory and Toledo, Edan and Shock, Jonathan and Buys, Jan. A Sequence Modelling Approach to Question Answering in Text-Based Games. Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022). 2022. doi:10.18653/v1/2022.wordplay-1.4

    work page doi:10.18653/v1/2022.wordplay-1.4 2022

  45. [45]

    Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents

    Teodorescu, Laetitia and Yuan, Xingdi and C \^o t \'e , Marc-Alexandre and Oudeyer, Pierre-Yves. Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents. Proceedings of the 3rd Wordplay: When Language Meets Games Workshop (Wordplay 2022). 2022. doi:10.18653/v1/2022.wordplay-1.5

    work page doi:10.18653/v1/2022.wordplay-1.5 2022

  46. [46]

    Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022

    2022

  47. [47]

    Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing

    Yuan, Shuzhou and Maronikolakis, Antonis and Sch. Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.1

    work page doi:10.18653/v1/2022.woah-1.1 2022

  48. [48]

    Towards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions

    Ashida, Mana and Komachi, Mamoru. Towards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.2

    work page doi:10.18653/v1/2022.woah-1.2 2022

  49. [49]

    G rease V ision: Rewriting the Rules of the Interface

    Datta, Siddhartha and Kollnig, Konrad and Shadbolt, Nigel. G rease V ision: Rewriting the Rules of the Interface. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.3

    work page doi:10.18653/v1/2022.woah-1.3 2022

  50. [50]

    Improving Generalization of Hate Speech Detection Systems to Novel Target Groups via Domain Adaptation

    Ludwig, Florian and Dolos, Klara and Zesch, Torsten and Hobley, Eleanor. Improving Generalization of Hate Speech Detection Systems to Novel Target Groups via Domain Adaptation. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.4

    work page doi:10.18653/v1/2022.woah-1.4 2022

  51. [51]

    `` Zo Grof ! '' : A Comprehensive Corpus for Offensive and Abusive Language in D utch

    Ruitenbeek, Ward and Zwart, Victor and Van Der Noord, Robin and Gnezdilov, Zhenja and Caselli, Tommaso. `` Zo Grof ! '' : A Comprehensive Corpus for Offensive and Abusive Language in D utch. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.5

    work page doi:10.18653/v1/2022.woah-1.5 2022

  52. [52]

    Counter- TWIT : An I talian Corpus for Online Counterspeech in Ecological Contexts

    Goffredo, Pierpaolo and Basile, Valerio and Cepollaro, Bianca and Patti, Viviana. Counter- TWIT : An I talian Corpus for Online Counterspeech in Ecological Contexts. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.6

    work page doi:10.18653/v1/2022.woah-1.6 2022

  53. [53]

    S tereo KG : Data-Driven Knowledge Graph Construction For Cultural Knowledge and Stereotypes

    Deshpande, Awantee and Ruiter, Dana and Mosbach, Marius and Klakow, Dietrich. S tereo KG : Data-Driven Knowledge Graph Construction For Cultural Knowledge and Stereotypes. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.7

    work page doi:10.18653/v1/2022.woah-1.7 2022

  54. [54]

    The subtle language of exclusion: Identifying the Toxic Speech of Trans-exclusionary Radical Feminists

    Lu, Christina and Jurgens, David. The subtle language of exclusion: Identifying the Toxic Speech of Trans-exclusionary Radical Feminists. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.8

    work page doi:10.18653/v1/2022.woah-1.8 2022

  55. [55]

    Lost in Distillation: A Case Study in Toxicity Modeling

    Chvasta, Alyssa and Lees, Alyssa and Sorensen, Jeffrey and Vasserman, Lucy and Goyal, Nitesh. Lost in Distillation: A Case Study in Toxicity Modeling. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.9

    work page doi:10.18653/v1/2022.woah-1.9 2022

  56. [56]

    Cleansing & expanding the HURTLEX (el) with a multidimensional categorization of offensive words

    Stamou, Vivian and Alexiou, Iakovi and Klimi, Antigone and Molou, Eleftheria and Saivanidou, Alexandra and Markantonatou, Stella. Cleansing & expanding the HURTLEX (el) with a multidimensional categorization of offensive words. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.10

    work page doi:10.18653/v1/2022.woah-1.10 2022

  57. [57]

    Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

    Israeli, Abraham and Tsur, Oren. Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.11

    work page doi:10.18653/v1/2022.woah-1.11 2022

  58. [58]

    Resources for Multilingual Hate Speech Detection

    Arango Monnar, Ayme and Perez, Jorge and Poblete, Barbara and Salda \ n a, Magdalena and Proust, Valentina. Resources for Multilingual Hate Speech Detection. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.12

    work page doi:10.18653/v1/2022.woah-1.12 2022

  59. [59]

    Enriching Abusive Language Detection with Community Context

    Saleem, Haji Mohammad and Kurrek, Jana and Ruths, Derek. Enriching Abusive Language Detection with Community Context. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.13

    work page doi:10.18653/v1/2022.woah-1.13 2022

  60. [60]

    DeTox: A Comprehensive Dataset for G erman Offensive Language and Conversation Analysis

    Demus, Christoph and Pitz, Jonas and Sch. DeTox: A Comprehensive Dataset for G erman Offensive Language and Conversation Analysis. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.14

    work page doi:10.18653/v1/2022.woah-1.14 2022

  61. [61]

    Multilingual H ate C heck: Functional Tests for Multilingual Hate Speech Detection Models

    R. Multilingual H ate C heck: Functional Tests for Multilingual Hate Speech Detection Models. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.15

    work page doi:10.18653/v1/2022.woah-1.15 2022

  62. [62]

    Distributional properties of political dogwhistle representations in S wedish BERT

    Hertzberg, Niclas and Cooper, Robin and Lindgren, Elina and R. Distributional properties of political dogwhistle representations in S wedish BERT. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.16

    work page doi:10.18653/v1/2022.woah-1.16 2022

  63. [63]

    Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions

    Khurana, Urja and Vermeulen, Ivar and Nalisnick, Eric and Van Noorloos, Marloes and Fokkens, Antske. Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.17

    work page doi:10.18653/v1/2022.woah-1.17 2022

  64. [64]

    Accounting for Offensive Speech as a Practice of Resistance

    Diaz, Mark and Amironesei, Razvan and Weidinger, Laura and Gabriel, Iason. Accounting for Offensive Speech as a Practice of Resistance. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.18

    work page doi:10.18653/v1/2022.woah-1.18 2022

  65. [65]

    Towards a Multi-Entity Aspect-Based Sentiment Analysis for Characterizing Directed Social Regard in Online Messaging

    Zheng, Joan and Friedman, Scott and Schmer-galunder, Sonja and Magnusson, Ian and Wheelock, Ruta and Gottlieb, Jeremy and Gomez, Diana and Miller, Christopher. Towards a Multi-Entity Aspect-Based Sentiment Analysis for Characterizing Directed Social Regard in Online Messaging. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:1...

    work page doi:10.18653/v1/2022.woah-1.19 2022

  66. [66]

    Flexible text generation for counterfactual fairness probing

    Fryer, Zee and Axelrod, Vera and Packer, Ben and Beutel, Alex and Chen, Jilin and Webster, Kellie. Flexible text generation for counterfactual fairness probing. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.20

    work page doi:10.18653/v1/2022.woah-1.20 2022

  67. [67]

    Users Hate Blondes: Detecting Sexism in User Comments on Online R omanian News

    Moldovan, Andreea and Cs. Users Hate Blondes: Detecting Sexism in User Comments on Online R omanian News. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.21

    work page doi:10.18653/v1/2022.woah-1.21 2022

  68. [68]

    Targeted Identity Group Prediction in Hate Speech Corpora

    Sachdeva, Pratik and Barreto, Renata and Von Vacano, Claudia and Kennedy, Chris. Targeted Identity Group Prediction in Hate Speech Corpora. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.22

    work page doi:10.18653/v1/2022.woah-1.22 2022

  69. [69]

    Revisiting Queer Minorities in Lexicons

    Ramesh, Krithika and Kumar, Sumeet and Khudabukhsh, Ashiqur. Revisiting Queer Minorities in Lexicons. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.23

    work page doi:10.18653/v1/2022.woah-1.23 2022

  70. [70]

    HATE - ITA : Hate Speech Detection in I talian Social Media Text

    Nozza, Debora and Bianchi, Federico and Attanasio, Giuseppe. HATE - ITA : Hate Speech Detection in I talian Social Media Text. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.24

    work page doi:10.18653/v1/2022.woah-1.24 2022

  71. [71]

    Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  72. [72]

    Changes in Tweet Geolocation over Time: A Study with Carmen 2.0

    Zhang, Jingyu and DeLucia, Alexandra and Dredze, Mark. Changes in Tweet Geolocation over Time: A Study with Carmen 2.0. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  73. [73]

    Extracting Mathematical Concepts from Text

    Collard, Jacob and de Paiva, Valeria and Fong, Brendan and Subrahmanian, Eswaran. Extracting Mathematical Concepts from Text. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  74. [74]

    Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data

    Ehghaghi, Malikeh and Rudzicz, Frank and Novikova, Jekaterina. Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  75. [75]

    Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis

    Eggleston, Chloe and O ' Connor, Brendan. Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  76. [76]

    Impact of Environmental Noise on A lzheimer ' s Disease Detection from Speech: Should You Let a Baby Cry?

    Novikova, Jekaterina. Impact of Environmental Noise on A lzheimer ' s Disease Detection from Speech: Should You Let a Baby Cry?. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  77. [77]

    Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets

    Pranesh, Raj. Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  78. [78]

    NTULM : Enriching Social Media Text Representations with Non-Textual Units

    Li, Jinning and Mishra, Shubhanshu and El-Kishky, Ahmed and Mehta, Sneha and Kulkarni, Vivek. NTULM : Enriching Social Media Text Representations with Non-Textual Units. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  79. [79]

    Robust Candidate Generation for Entity Linking on Short Social Media Texts

    Hebert, Liam and Makki, Raheleh and Mishra, Shubhanshu and Saghir, Hamidreza and Kamath, Anusha and Merhav, Yuval. Robust Candidate Generation for Entity Linking on Short Social Media Texts. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

  80. [80]

    T rans POS : Transformers for Consolidating Different POS Tagset Datasets

    Li, Alex and Bankole-Hameed, Ilyas and Singh, Ranadeep and Ng, Gabriel and Gupta, Akshat. T rans POS : Transformers for Consolidating Different POS Tagset Datasets. Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022). 2022

    2022

Showing first 80 references.