README.md

metadata

YAML tags: null
annotations_creators:
  - expert-generated
language:
  - ca
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
pretty_name: InToxiCat
tags:
  - abusive-language-detection
  - abusive-language
  - toxic-language-detection
  - toxicity-detection
task_categories:
  - text-classification
  - token-classification

Dataset Card for InToxiCat

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Example
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Website: https://zenodo.org/record/7973926 #TODO
• Point of Contact: langtech@bsc.es

Dataset Summary

InToxiCat is a dataset for the detection of abusive language (defined by the aim to harm someone, individual, group, etc.) in Catalan, produced by the BSC LangTech unit.

The dataset consists of 29,809 sentences obtained from internet forums annotated as to whether or not they are abusive. The 6047 instances annotated as abusive are further annotated for the following features: abusive span, target span, target type and the implicit or explicit nature of the abusiveness in the message.

The dataset is split, in a balanced abusive/non-abusive distribution, into 23,847 training samples, 2981 validation samples, and 2981 test samples.

This work is licensed under a Attribution-ShareAlike 4.0 International License.

Supported Tasks and Leaderboards

Abusive Language Detection

Languages

The dataset is in Catalan (ca-CA).

Dataset Structure

Data Instances

Three JSON files, one for each split.

Example:

    
    {
    "id": "9472844_16_0",
    "context": "Aquest tiu no té ni puta idea del que és una guerra ni del que s'espera d'un soldat.I què s'empatolla de despeses mèdiques. A veure si li passaré com al Hollande i sortiré la factura del seu perruquer (o taxidermista, no sé)",
    "sentence": "Aquest tiu no té ni puta idea del que és una guerra ni del que s'espera d'un soldat.I què s'empatolla de despeses mèdiques.",
    "topic": "Internacional",
    "key_words": [
      "puta"
    ],
    "annotation": {
      "is_abusive": "abusive",
      "abusiveness_agreement": "full",
      "context_needed": "no",
      "abusive_spans": [
        [
          "no té ni puta idea",
          "11:29"
        ]
      ],
      "target_spans": [
        [
          "Aquest tiu",
          "0:10"
        ]
      ],
      "target_type": [
        "INDIVIDUAL"
      ],
      "is_implicit": "yes"
    }
  }
  

Data Fields

• id (a string feature): unique identifier of the instance.
• context (a string feature): complete text message from the user surrounding the sentence (it can coincide totally or only partially with the sentence).
• sentence (a string feature): text message where the abusiveness is evaluated.
• topic (a string feature): category from Racó Català forums where the sentence comes from.
• keywords (a list of strings): keywords used to select the candidate messages to annotate.
• context_needed (a string feature): "yes" / "no" if all the annotators consulted / did not consult the context to decide on the sentence's abusiveness, "maybe" if there was not agreement about it.
• is_abusive (a bool feature): "abusive" or "not_abusive"
• abusiveness_agreement (a string feature): "full" if the two annotators agreed on the abusiveness/not-abusiveness of the sentence, and "partial" if the abusiveness had to be decided by a third annotator.
• abusive_spans (a dictionary with field 'text' (list of strings) and 'index' (list of strings)): the sequence of words that attribute to the text's abusiveness.
• is_implicit (a string): whether the abusiveness is explicit (contains a profanity, slur or threat) or implicit (does not contain a profanity or slur, but is likely to contain irony, sarcasm or similar resources)
• target_spans (a dictionary with field 'text' (list of strings) and 'index' (list of strings)): if found in the message, the sequence(s) of words that refer to the target of the text's abusiveness
• target_type (a dictionary with field 'text' (list of strings) and 'index' (list of strings)): three possible categories. The categories are non-exclusive, as some targets may have a dual identity and more than one target may be detected in a single message.
  • individual: a famous person, a named person or an unnamed person interacting in the conversation.
  • group: considered to be a unit based on the same ethnicity, gender or sexual orientation, political affiliation, religious belief or something else.
  • other; e.g. an organization, a situation, an event, or an issue

Data Splits

• train.json: 23847 examples
• dev.json: 2981 examples
• test.json: 2981 examples

Dataset Creation

Curation Rationale

We created this dataset to contribute to the development of language models in Catalan, a low-resource language.

Source Data

Initial Data Collection and Normalization

The sentences to be annotated were collected from Racó Català forums using a list of keywords (provided in Zenodo). The messages belong to different categories of Racó Català, specified in the "topic" field of the dataset. The length of the messages varies from one sentence to several sentences.

Who are the source language producers?

Anonymized users from Racó Català forums.

Annotations

Annotation process

The annotation process was divided into the following two tasks, carried out in sequential order:

Task 1. The sentences (around 30.000) were annotated by two annotators as either abusive or not abusive. In case of ambiguity in the sentence, the annotators had the possibility to consult the context, i.e. the whole message of the user (if the sentence to be annotated was a segment contained in the message). In cases where annotators 1 and 2 disagreed about the abusiveness of a message, it was annotated by a third annotator. As a result, the sentences that are ultimately considered abusive are those that were initially annotated as abusive by both annotators or, in the case of an initial disagreement between them, those that were resolved as abusive by the third annotator.

Task 2. The sentences annotated as abusive (6047) in Task 1 were further annotated by the two main annotators for the following features, explained in the Summary section: abusive spans, implicit/explicit abusiveness, target spans, and target type.

The annotation guidelines are published and available on Zenodo.

Who are the annotators?

The annotators were qualified professionals with university education and a demonstrably excellent knowledge of Catalan (minimum level C1 or equivalent).

Personal and Sensitive Information

No personal or sensitive information included.

Considerations for Using the Data

Social Impact of Dataset

We hope this dataset contributes to the development of language models in Catalan, a low-resource language.

Discussion of Biases

[N/A]

Other Known Limitations

[N/A]

Additional Information

Dataset Curators

Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es)

This work was funded by the Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya within the framework of Projecte AINA.

Licensing Information

This work is licensed under a Attribution-ShareAlike 4.0 International License

Citation Information

DOI

Contributions

[N/A]