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Data set for automatic detection of online misogynistic speech.

Theo Lynn1, Patricia Takako Endo1, Pierangelo Rosati1

  • 1Dublin City University (DCU), Irish Institute of Digital Business (dotLAB), Brazil.

Data in Brief
|September 12, 2019
PubMed
Summary

This study analyzed 2285 Urban Dictionary entries from 1999-2016. Researchers classified definitions as misogynistic or not, creating a dataset for analyzing online hate speech.

Keywords:
Hate speechMisogynistic speechMisogyny detectionOnline speechUrban dictionary

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Area of Science:

  • Computational Linguistics
  • Sociology
  • Digital Humanities

Background:

  • Urban Dictionary serves as a rich source for contemporary language use.
  • Understanding online misogyny requires analyzing user-generated content.
  • Previous research has explored online harassment, but large-scale datasets of specific platforms are scarce.

Purpose of the Study:

  • To create a labeled dataset of Urban Dictionary definitions to identify misogynistic content.
  • To provide a resource for researchers studying online misogyny and hate speech.
  • To facilitate computational analysis of gender-based online toxicity.

Main Methods:

  • Collected 2285 definitions from Urban Dictionary (1999-2016).
  • Three domain-expert researchers independently classified each definition as misogynistic (1) or non-misogynistic (0).
  • Compiled the data into a publicly available table with definitions and classifications.

Main Results:

  • A dataset of 2285 definitions was successfully curated and classified.
  • The dataset provides a binary classification of misogynistic content.
  • The data is ready for further computational analysis and model training.

Conclusions:

  • The created dataset offers a valuable resource for studying online misogyny.
  • This work enables quantitative research into the nature and prevalence of misogynistic language on Urban Dictionary.
  • The findings support the need for continued investigation into digital platforms as sites of gendered hate speech.