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Building towards Automated Cyberbullying Detection: A Comparative Analysis.

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Anonymous social media fuels cyberbullying. This survey analyzes automated detection methods, including emoji and self-supervised learning (SSL) techniques, to combat online harassment effectively.

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Area of Science:

  • Computer Science
  • Social Computing
  • Natural Language Processing

Background:

  • Social media anonymity enables participation but also facilitates cyberbullying and hate speech.
  • The rise of online harassment necessitates effective cyberbullying detection strategies.
  • Automated detection methods are crucial for mitigating the detrimental effects of cyberbullying.

Purpose of the Study:

  • To provide a comprehensive survey of automated cyberbullying detection techniques.
  • To analyze various perspectives including data annotation, preprocessing, and feature engineering.
  • To explore the impact of emojis and self-supervised learning (SSL) on detection performance.

Main Methods:

  • Comparative analysis of existing automated cyberbullying detection techniques.
  • Discussion on the role of data annotation, preprocessing, and feature engineering.
  • Exploration of emoji integration and self-supervised learning (SSL) for enhanced detection.

Main Results:

  • Anonymity on social media presents a dual challenge for user engagement and online safety.
  • Emojis significantly influence sentiment analysis and text comprehension, impacting cyberbullying detection.
  • Self-supervised learning (SSL) shows promise as an effective annotation technique for cyberbullying detection.

Conclusions:

  • Automated cyberbullying detection requires a multi-faceted approach considering data, features, and advanced learning techniques.
  • Incorporating emojis and leveraging SSL can improve the accuracy and efficiency of cyberbullying detection systems.
  • Further research into these areas is vital for developing robust defenses against online harassment.