Translated vs non-translated method for multilingual hate speech identification in Twitter

Muhammad Okky Ibrohim, Indra Budi

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Nowadays social media is often misused to spread hate speech. Spreading hate speech is an act that needs to be handled in a special way because it can undermine or discriminate other people and cause conflict that leading to both material and immaterial losses. There are several challenges in building a hate speech identification system; one of them is identifying hate speech in multilingual scope. In this paper, we adapt and compare two methods in multilingual text classification which are translated (with and without language identification) and non-translated method for multilingual hate speech identification (including Hindi, English, and Indonesian language) using machine learning approach. We use some classification algorithms (classifiers) namely Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) with word n-grams and char n-grams (character n-grams) as feature extraction. Our experiment result shows that the non-translated method gives the best result. However, the use of non-translated method needs to be reconsidered because this method needs more cost for data collection and annotation. Meanwhile, translated without language identification method give a poor result. To address this problem, we combine translated method with monolingual hate speech identification, and the experiment result shows that this approach can increase the multilingual hate speech identification performance compared to translate without language identification. This paper discusses the advantages and disadvantages for all method and the future works to enhance the performance in multilingual hate speech identification.

Original languageEnglish
Pages (from-to)1116-1123
Number of pages8
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Issue number4
Publication statusPublished - 1 Jan 2019


  • Machine learning
  • Multilingual hate speech identification
  • Social media


Dive into the research topics of 'Translated vs non-translated method for multilingual hate speech identification in Twitter'. Together they form a unique fingerprint.

Cite this