Abstract

As part of hate speech, dangerous speech is any expression that can increase the risk of committing violence against other people. So far, hate speech research only explains whether some sentences is categorized as hate speech. It does not explain aspects of the sentences that make them called dangerous speech. Aspects of dangerous speech are social context, historical context, dehumanization, the accusation in the mirror, women and children attack, loyalty to the group, and group threat. This study uses the multi-label text classification method to determine dangerous speeches on Twitter texts based on seven aspects. Then, we assign a weighted score from those aspects to differentiate dangerous and hate speech. Based on the test results show the best performance is the Naive Bayes method with label-based subset accuracy (±36 %), instance-based (average) accuracy (±86%) and classification accuracy (±77%). However, even though Naive Bayes has the best performance in terms of instance based (average) accuracy, the average difference between all methods with Naive Bayes is only ± 0.014, this indicates that other methods also produce quite good performance.

Original languageEnglish
Title of host publication2022 10th International Conference on Information and Communication Technology, ICoICT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages179-183
Number of pages5
ISBN (Electronic)9781665481656
DOIs
Publication statusPublished - 2022
Event10th International Conference on Information and Communication Technology, ICoICT 2022 - Virtual, Online, Indonesia
Duration: 2 Aug 20223 Aug 2022

Publication series

Name2022 10th International Conference on Information and Communication Technology, ICoICT 2022

Conference

Conference10th International Conference on Information and Communication Technology, ICoICT 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period2/08/223/08/22

Keywords

  • dangerous speech
  • multi-label text classification
  • twitter texts
  • weighted sum model

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