Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

Semi Park, Kyungho Lee

Research output: Contribution to journalArticlepeer-review


According to BBC News, online hate speech increased by 20% during the COVID-19 pandemic. Hate speech from anonymous users can result in psychological harm, including depression and trauma, and can even lead to suicide. Malicious online comments are increasingly becoming a social and cultural problem. It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level. To achieve a healthy and safe Internet environment, studies should focus on institutional and technical topics. The detection of toxic comments can create a safe online environment. In this study, to detect malicious comments, we used approximately 9,400 examples of hate speech from a Korean corpus of entertainment news comments. We developed toxic comment classification models using supervised learning algorithms, including decision trees, random forest, a support vector machine, and K-nearest neighbors. The proposed model uses random forests to classify toxic words, achieving an F1-score of 0.94. We analyzed the trained model using the permutation feature importance, which is an explanatory machine learning method. Our experimental results confirmed that the toxic comment classifier properly classified hate words used in Korea. Using this research methodology, the proposed method can create a healthy Internet environment by detecting malicious comments written in Korean.

Original languageEnglish
Pages (from-to)813-826
Number of pages14
JournalComputers, Materials and Continua
Issue number1
Publication statusPublished - 2023

Bibliographical note

Funding Information:
Funding Statement: This study was supported by a grant-in-aid of HANWHASYSTEMS.

Publisher Copyright:
© 2023 Tech Science Press. All rights reserved.


  • healthy internet environment
  • machine learning
  • Toxic comments
  • toxic text classification

ASJC Scopus subject areas

  • Biomaterials
  • Modelling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering


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