MARIANNE-INRIA/pragmaticsAggressionRedditConversations

Dataset Summary

pragmaticsAggressionRedditConversations is an English corpus of pragmatically annotated online Reddit conversations. Introduced by Fu and Ollagnier (2026), it consists of complete Reddit conversation threads enriched with message-level pragmatic annotations. The annotation framework draws on Speech Act Theory, capturing both illocutionary acts and perlocutionary effects, going beyond surface-level toxicity labels to model the communicative dynamics that underlie online aggression.

The dataset contains 124 Reddit conversations (1,990 messages), sampled to include discussions that escalate to toxicity. A structured human annotation protocol was applied to a test split of 31 conversations, alongside an equal-sized sample from the CGA-Wiki dataset (31 conversations, see Zhang et al., 2018 ), for a combined human-annotated subset of 62 conversations. Annotation was conducted at the message level by annotators with backgrounds in NLP. The remaining conversations are annotated using GPT-3, GPT-4, and GPT-5.

Supported Tasks

• Toxicity / hate speech detection: The pragmatic annotations enable context-aware modelling of toxic language by capturing pragmatic inferences, interactional dynamics, and speaker intentionality beyond surface lexical features.
• Conversation analysis and derailment prediction: The full thread structure and pragmatic labels support modelling how conversations escalate toward toxicity.

Languages

English (en) only.

Dataset Structure

Data Fields

pragmaticsAggressionRedditConversations

Field	Type	Description
Subreddit	string	Subreddit where the conversation took place
Time	string	Timestamp of the message
Message_ID	string	Unique message identifier (can be used to retrieve original content via the Reddit API)
Message_Author	string	Anonymised author identifier
Message_Content	string	Original message content is not included directly; it can be retrieved using the Message_ID field via the Reddit API.
Reply_To	string	Message ID of the parent message this reply is addressed to
Pragmatic_Inferences	string	Full list of pragmatic inferences associated with the message
most_salient_inference	string	The most prominent pragmatic inference
inference_type	categorical	Speech act category (e.g., directive, representative, expressive)
as_intended	bool	Whether the inference was intended by the speaker
PRE/IMP	categorical	Presupposition or implicature label
aggressive	categorical	covertly aggressive (CAG), overtly aggressive (OAG) or not aggressive (NAG)
note	string	Free-text annotator notes
annot1	categorical	maxim violation/politeness strategy assigned by annotator 1 (test split only)
annot1_comment	string	Comment from annotator 1 -- mainly used for assigning relations between maxim violations (test split only)
annot2	categorical	maxim violation/politeness strategy by annotator 2 (test split only)
annot2_comment	string	Comment from annotator 2 -- mainly used for assigning relations between maxim violations (test split only)

Note: The four final columns (annot1, annot1_comment, annot2, annot2_comment) are populated only for the test split, which received full human annotation.

CGA-Wiki

For a full description of the CGA-Wiki dataset (Zhang et al., 2018) structure, please refer to the ConvoKit documentation. In this work, an additional layer of pragmatic annotation was applied to a subset of 31 CGA-Wiki conversations to match the Reddit test split. This annotation layer is released as part of the present dataset; detailed documentation of the annotation scheme will be published in future work.

Initial Data Collection and Normalization

Reddit conversations were sampled from publicly available discussion threads, filtered to include threads that contain at least one toxic message. Full conversation context (preceding and following messages) is preserved for each thread. The dataset is anonymised; original message content can be retrieved using the Message_ID field via the Reddit API. Conversations from the CGA dataset were randomly sampled to match the size of the Reddit test set.

Annotation

For full annotation guidelines, refer to the GitHub repository. See Fu & Ollagnier (2026) for full details.

For the test split, an additional annotation layer was applied at the message level: toxic messages are annotated with Gricean maxim violations, and non-toxic messages with politeness strategies. This layer is not yet described in a published paper; full documentation is forthcoming.

Personal and Sensitive Information

The Reddit dataset is anonymised. Original message content is not included directly; it can be retrieved using the Message_ID field via the Reddit API.

The CGA-Wiki conversations are reproduced as originally published by Zhang et al. (2018); only an additional annotation layer has been added.

Considerations for Using the Data

Discussion of Biases

• Pragmatic annotations are inherently subjective and may reflect the annotators' own linguistic and cultural backgrounds.

Other Known Limitations

• English only: The dataset does not cover multilingual or code-switched online discourse.
• Platform-specific: Reddit conversation and Wikipedia talk page norms differ from those of other online forums, social media platforms, or offline contexts.
• Partial human annotation: Only the test split (31 conversations) received full human annotation; the remaining 93 conversations were annotated automatically using GPT models.

Licensing Information

This dataset is distributed under the CC BY-NC 4.0 license.

Citation Information

If you use this dataset, please cite the following paper:

@inproceedings{fu-etal-2026-contextualizing,
  title = {Contextualizing Toxicity: An Annotation Framework for Unveiling Pragmatics in Conversations of Online Discussion Forums},
  author = {Fu, Yingxue and Ollagnier, Anais},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)},
  month = {May},
  year = {2026},
  pages = {3961--3974},
  address = {Palma, Mallorca, Spain},
  publisher = {European Language Resources Association (ELRA)},
  editor = {Piperidis, Stelios and Bel, Núria and van den Heuvel, Henk and Ide, Nancy and Krek, Simon and Toral, Antonio},
  doi = {10.63317/2iicz26v9ckq},
  abstract = {The role of context has attracted increasing attention in research on toxicity detection. Interpreting toxic language remains a complex and multifaceted challenge, shaped by numerous linguistic, contextual, and social factors. However, current approaches often define "context" narrowly, focusing primarily on surface lexical cues such as hate lexicons, profanity markers, or sentiment polarity. These features, while useful, are insufficient to capture the interactional dynamics, user behaviors, and intentionality that shape such phenomena. To address this gap, this paper introduces a novel and systematic annotation framework, grounded in Speech Act Theory (Austin, 1962), aimed at deciphering the illocutionary and perlocutionary dimensions of conversation, which are unexplored in existing studies. We apply this framework to a new dataset of complete Reddit conversation threads, sampled to include discussions that turn toxic (124 conversations, 1990 messages). We evaluate the performance of GPT models (GPT-3, GPT-4, and GPT-5) on this challenging annotation task, providing insights into how large language models capture pragmatic and contextual dimensions of online toxicity.}
}

Please also cite the upstream datasets where appropriate:

@inproceedings{zhang2018conversations,
  title={Conversations gone awry: Detecting early signs of conversational failure},
  author={Zhang, Justine and Chang, Jonathan and Danescu-Niculescu-Mizil, Cristian and Dixon, Lucas and Hua, Yiqing and Taraborelli, Dario and Thain, Nithum},
  booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={1350--1361},
  year={2018}
}

Contact

• Yingxue Fu — fuyingxue321@gmail.com
• Anaïs Ollagnier — anais.ollagnier@inria.fr