Prasetyo, VR and Samudra, AH orcid.org/0000-0002-1194-9006 (2022) Hate speech content detection system on Twitter using K-nearest neighbor method. In: AIP Conference Proceedings. INTERNATIONAL CONFERENCE ON INFORMATICS, TECHNOLOGY, AND ENGINEERING 2021 (InCITE 2021), 25-26 Aug 2021, Surabaya, Indonesia. American Institute of Physics ISBN 9780735441804
Abstract
Twitter is a social media platform that many Indonesians use to express their thoughts on a variety of topics. In Indonesia, the use of social media is governed by a law known as Information and Electronic Transactions Law. However, until now, the implementation of this law has been subpar. This is because there are still violations occurring, and no legal action has been taken against these violations. Hate speech is a common violation on Twitter. The goal of this research is to create a system that can detect potential violations of content on Twitter, particularly content containing hate speech. The k-nearest neighbor (KNN) method was used in this research, along with the feature extraction method TF-IDF. The system built will detect whether the tweet you want to post violates a specific article in the Information and Electronic Transactions Law. Based on model validation, model classifier built has accuracy value is 67.86%, with K value in the KNN method is 10. Meanwhile, based on user validation, the system created has an accuracy of 77%.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Law (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Nov 2022 15:26 |
Last Modified: | 23 Nov 2022 15:26 |
Status: | Published |
Publisher: | American Institute of Physics |
Identification Number: | 10.1063/5.0080185 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193514 |