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Examining Bilingual Language Control Using the Stroop Task
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A survey on multi-lingual offensive language detection.

Khouloud Mnassri1, Reza Farahbakhsh1, Razieh Chalehchaleh1

  • 1Samovar, Telecom SudParis, Institut Polytechnique de Paris, Palaiseau, France.

Peerj. Computer Science
|April 25, 2024
PubMed
Summary
This summary is machine-generated.

Detecting offensive language across multiple languages is challenging due to linguistic diversity. This survey reviews current methods and datasets for multilingual offensive language detection, proposing future research directions for safer online communication.

Keywords:
Hate speechLiterature reviewMultilingualismOffensive languageSocial media

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Social Media Analysis

Background:

  • The increasing prevalence of offensive content online necessitates effective detection methods.
  • Multilingual offensive language presents unique challenges due to linguistic and cultural diversity.
  • Existing detection systems often struggle with the nuances of various languages and cultural contexts.

Purpose of the Study:

  • To survey the current landscape of multilingual offensive language detection.
  • To analyze existing approaches, datasets, and community challenges in this domain.
  • To identify future research directions for improved detection systems.

Main Methods:

  • Comprehensive literature review of multilingual offensive language detection studies.
  • Analysis of existing datasets and their suitability for cross-lingual tasks.
  • Identification and categorization of technical, cultural, and linguistic challenges.
  • Exploration of community-driven efforts and shared tasks.

Main Results:

  • An overview of various multilingual offensive language detection techniques and their performance.
  • A critical assessment of the limitations and biases within current datasets.
  • Identification of key challenges hindering effective cross-lingual detection.
  • A curated list of resources and datasets for researchers in the field.

Conclusions:

  • Multilingual offensive language detection requires addressing complex linguistic and cultural factors.
  • Further research is needed to develop robust and equitable detection models.
  • Developing standardized benchmarks and diverse datasets is crucial for progress.
  • The goal is to foster safer digital communication globally through advanced detection technologies.