README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - text-classification
language:
  - en
  - es
tags:
  - hatespeech
pretty_name: Multilingual detection of hate speech against immigrants and women in Twitter
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: text
      dtype: string
    - name: target
      dtype: string
    - name: language
      dtype: string
    - name: HS
      dtype: int64
    - name: TR
      dtype: int64
    - name: AG
      dtype: int64
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: train
      num_bytes: 2629983
      num_examples: 13500
    - name: dev
      num_bytes: 310342
      num_examples: 1500
    - name: test
      num_bytes: 902887
      num_examples: 4570
  download_size: 2190255
  dataset_size: 3843212
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*

This is the dataset for SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (HatEval).

CodaLab page

Shared Task Report

Shared Task

Hate Speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristic such as race, color, ethnicity, gender, sexual orientation, nationality, religion, or other characteristics. Given the huge amount of user-generated contents on the Web, and in particular on social media, the problem of detecting, and therefore possibly limit the Hate Speech diffusion, is becoming fundamental, for instance for fighting against misogyny and xenophobia.

The proposed task consists in Hate Speech detection in Twitter but featured by two specific different targets, immigrants and women, in a multilingual perspective, for Spanish and English. The task will be articulated around two related subtasks for each of the involved languages: a basic task about Hate Speech, and another one where fine-grained features of hateful contents will be investigated in order to understand how existing approaches may deal with the identification of especially dangerous forms of hate, i.e. those where the incitement is against an individual rather than against a group of people, and where an aggressive behavior of the author can be identified as a prominent feature of the expression of hate. Participants will be asked to identify, on the one hand, if the target of hate is a single human or a group of persons, on the other hand, if the message author intends to be aggressive, harmful, or even to incite, in various forms, to violent acts against the target.

• TASK A - Hate Speech Detection against Immigrants and Women: a two-class (or binary) classification where systems have to predict whether a tweet in English or in Spanish with a given target (women or immigrants) is hateful or not hateful.
• TASK B - Aggressive behavior and Target Classification: where systems are asked first to classify hateful tweets for English and Spanish (e.g., tweets where Hate Speech against women or immigrants has been identified) as aggressive or not aggressive, and second to identify the target harassed as individual or generic (i.e. single human or group).

Organizers

Organizers:

• Valerio Basile, Università degli Studi di Torino, Turin (Italy)
• Cristina Bosco, Università degli Studi di Torino, Turin (Italy)
• Elisabetta Fersini, Università degli Studi di Milano Bicocca, Milan (Italy)
• Debora Nozza, Università degli Studi di Milano Bicocca, Milan (Italy)
• Viviana Patti, Università degli Studi di Torino, Turin (Italy)
• Francisco Rangel, Autoritas Consulting S.A., Madrid (Spain)
• Paolo Rosso, Universidad Politecnica de Valencia, Valencia (Spain)
• Manuela Sanguinetti, Università degli Studi di Torino, Turin (Italy)

Data

The entire HatEval dataset is composed of 19,600 tweets, 13,000 for English and 6,600 for Spanish. They are distributed across the targets as follows: 9,091 about immigrants and 10,509 about women.

The figures below show the distribution of the labels in the training and development set data according to the different targets of hate (woman and immigrants, respectively).

Annotation

There are three fields annotated with binary labels:

• Hate Speech (HS):
  • 0: absence of hate speech
  • 1: presence of hate speech
• Target (TR):
  • 0: HS towards a group
  • 1: HS towards an individual
• Aggressivenes (AG):
  • 0: absence of aggressive speech
  • 1: presence of aggressive speech