research-article

Hate Speech Identification using the Hate Codes for Indonesian Tweets

Authors:

Nur Indah Pratiwi,

Indra Budi,

Meganingrum Arista JiwanggiAuthors Info & Claims

DSIT 2019: Proceedings of the 2019 2nd International Conference on Data Science and Information Technology

Pages 128 - 133

https://doi.org/10.1145/3352411.3352432

Published: 19 July 2019 Publication History

Get Access

Abstract

The hate speech has become the major source of negativity spread in all over the social media. As the social media becomes aware of this issue, they gradually build several new regulations to handle the spread of hate speech e.g. by automatically blocking or suspending the accounts or posts containing hate speech. However, the social media users have become more creative in expressing the hate speech. To avoid the social media regulations regarding the hate speech, users usually use some special codes to interact with each other. This study aims to utilize the hate codes to identify the hate speech on the social media data. We used the Indonesian tweets as the dataset. We utilized Logistic Regression, Support Vector Machine, Naïve Bayes, and Random Forest Decision Tree as the classifiers. The highest F-Measure score for the hate speech identification was 80.71% by using the hate code feature combined with Logistic Regression as the classifier.

References

[1]

Asosiasi Penyelenggara Jasa Internet Indonesia., "Infografis penetrasi perilaku pengguna internet indonesia," 2017.

Google Scholar

[2]

Komisi Nasional Hak Asasi Manusia., "Buku saku penanganan ujaran kebencian hate speech," 2015.

Google Scholar

[3]

R. Magu, K. Joshi, and J. Luo, "Detecting the hate code on social media," CoRR, vol. abs/1703.05443, 2017. {Online}. Available: http://arxiv.org/abs/1703.05443

Google Scholar

[4]

S. H. Pratiwi, "Deteksi ujaran kebencian terkait agama pada Tweet berbahasa indonesia menggunakan algoritma Naive Bayes dan Support Vector Machine," Master's thesis, Fakultas Ilmu Komputer, Universitas Indonesia, 2016.

Google Scholar

[5]

I. Alfina, R. Mulia, M. I. Fanany, and Y. Ekanata, "Hate speech detection in the indonesian language: A dataset and preliminary study," 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2017.

Google Scholar

[6]

H. Watanabe, M. Bouazizi, and T. Ohtsuki, "Hate speech on twitter: A pragmatic approach to collect hateful and offensive expressions and perform hate speech detection," IEEE Access, vol. 6, pp. 13825--13835, 2018.

Crossref

Google Scholar

[7]

P. Burnap and M. L. Williams, "Us and them: identifying cyber hate on twitter across multiple protected characteristics," EPJ Data Science, vol. 5, no. 1, p. 11, Mar 2016. {Online}. Available:

Crossref

Google Scholar

[8]

R. Mulia, "Deteksi ujaran kebencian pada tweet berbahasa indonesia menggunakan pendekatan machine learning," Depok, Universitas Indonesia, 2017.

Google Scholar

[9]

M. O. Ibrohim and I. Budi, "A dataset and preliminaries study for abusive language detection in indonesian social media," Procedia Computer Science, vol. 135, pp. 222--229, 2018, the 3rd International Conference on Computer Science and Computational Intelligence (ICCSCI 2018): Empowering Smart Technology in Digital Era for a Better Life. {Online}. Available: http://www.sciencedirect.com/science/article/pii/S1877050918314583

Crossref

Google Scholar

[10]

E. Sazany and I. Budi, "Deep learning-based implementation of hate speech identification on texts in indonesian: Preliminary study," in International Conference on Applied Information Technology and Innovation (ICAITI 2018). Accepted, 2018.

Google Scholar

[11]

I. Alfina, D. Sigmawaty, F. Nurhidayati, and A. N. Hidayanto, "Utilizing hashtags for sentiment analysis of tweets in the political domain," in Proceedings of the 9th International Conference on Machine Learning and Computing, ser. ICMLC 2017. New York, NY, USA: ACM, 2017, pp. 43--47. {Online}. Available:

Digital Library

Google Scholar

[12]

Tala, F.Z., "A study of stemming effects on information retrieval in bahasa indonesia." 2003.

Google Scholar

Cited By

View all

Kyaw NThu YOo TChanlekha HOkumura MSupnithi T(2024)Enhancing Hate Speech Classification in Myanmar Language through Lexicon-Based Filtering2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE61278.2024.10613636(316-323)Online publication date: 19-Jun-2024
https://doi.org/10.1109/JCSSE61278.2024.10613636
Devi VKannimuthu SMadasamy A(2024)The Effect of Phrase Vector Embedding in Explainable Hierarchical Attention-Based Tamil Code-Mixed Hate Speech and Intent DetectionIEEE Access10.1109/ACCESS.2024.334995812(11316-11329)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3349958
Fetahi EHamiti MSusuri AShehu VBesimi A(2023)Automatic Hate Speech Detection using Natural Language Processing: A state-of-the-art literature review2023 12th Mediterranean Conference on Embedded Computing (MECO)10.1109/MECO58584.2023.10155070(1-6)Online publication date: 6-Jun-2023
https://doi.org/10.1109/MECO58584.2023.10155070
Show More Cited By

Index Terms

Recommendations

Spread of Hate Speech in Online Social Media
WebSci '19: Proceedings of the 10th ACM Conference on Web Science

Hate speech is considered to be one of the major issues currently plaguing the online social media. With online hate speech culminating in gruesome scenarios like the Rohingya genocide in Myanmar, anti-Muslim mob violence in Sri Lanka, and the ...
A Measurement Study of Hate Speech in Social Media
HT '17: Proceedings of the 28th ACM Conference on Hypertext and Social Media

Social media platforms provide an inexpensive communication medium that allows anyone to quickly reach millions of users. Consequently, in these platforms anyone can publish content and anyone interested in the content can obtain it, representing a ...
Identifying and Categorising Profane Words in Hate Speech
ICCDA '18: Proceedings of the 2nd International Conference on Compute and Data Analysis

This study attempts to explore the different types of Hate Speech appearing in social media by identifying profane words used in hate speech. This study also compares the profane words used in different generations to assist in identifying the user's ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

DSIT 2019: Proceedings of the 2019 2nd International Conference on Data Science and Information Technology

July 2019

280 pages

ISBN:9781450371414

DOI:10.1145/3352411

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

The Hong Kong Polytechnic: The Hong Kong Polytechnic University
Natl University of Singapore: National University of Singapore

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

DSIT 2019

DSIT 2019: 2019 2nd International Conference on Data Science and Information Technology

July 19 - 21, 2019

Seoul, Republic of Korea

Acceptance Rates

DSIT 2019 Paper Acceptance Rate 43 of 95 submissions, 45%;

Overall Acceptance Rate 114 of 277 submissions, 41%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
196
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Kyaw NThu YOo TChanlekha HOkumura MSupnithi T(2024)Enhancing Hate Speech Classification in Myanmar Language through Lexicon-Based Filtering2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE61278.2024.10613636(316-323)Online publication date: 19-Jun-2024
https://doi.org/10.1109/JCSSE61278.2024.10613636
Devi VKannimuthu SMadasamy A(2024)The Effect of Phrase Vector Embedding in Explainable Hierarchical Attention-Based Tamil Code-Mixed Hate Speech and Intent DetectionIEEE Access10.1109/ACCESS.2024.334995812(11316-11329)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3349958
Fetahi EHamiti MSusuri AShehu VBesimi A(2023)Automatic Hate Speech Detection using Natural Language Processing: A state-of-the-art literature review2023 12th Mediterranean Conference on Embedded Computing (MECO)10.1109/MECO58584.2023.10155070(1-6)Online publication date: 6-Jun-2023
https://doi.org/10.1109/MECO58584.2023.10155070
Ibrohim MBudi I(2023)Hate speech and abusive language detection in Indonesian social media: Progress and challengesHeliyon10.1016/j.heliyon.2023.e18647(e18647)Online publication date: Jul-2023
https://doi.org/10.1016/j.heliyon.2023.e18647
Rodela ANguyen HFarid DHuda M(2023)Bangla Social Media Cyberbullying Detection Using Deep LearningIntelligent Systems and Data Science10.1007/978-981-99-7649-2_13(170-184)Online publication date: 31-Oct-2023
https://doi.org/10.1007/978-981-99-7649-2_13
Muzakir AAdi KKusumaningrum R(2022)Classification of Hate Speech Language Detection on Social Media: Preliminary Study for ImprovementEmerging Trends in Intelligent Systems & Network Security10.1007/978-3-031-15191-0_14(146-156)Online publication date: 1-Sep-2022
https://doi.org/10.1007/978-3-031-15191-0_14
Sangwan SBhatia M(2021) Soft computing for abuse detection using cyber‐physical and social big data in cognitive smart cities Expert Systems10.1111/exsy.1276639:5Online publication date: 18-Jul-2021
https://doi.org/10.1111/exsy.12766
Kishara Buddika Jayasanka HRashitha Dileeshan Batugedara BRuwan Diyunuge KYehan Malith Jayasekara TLunugalage DSamaratunge Arachchilllage U(2021)Intelligent Violence Video Detection System2021 3rd International Conference on Advancements in Computing (ICAC)10.1109/ICAC54203.2021.9671189(270-275)Online publication date: 9-Dec-2021
https://doi.org/10.1109/ICAC54203.2021.9671189
Jiang XYu JJiang J(2021)Research on feature point generation and matching method optimization in image matching algorithmWireless Networks10.1007/s11276-021-02688-xOnline publication date: 19-Jul-2021
https://doi.org/10.1007/s11276-021-02688-x
Rini RUtami EHartanto A(2020)Systematic Literature Review Of Hate Speech Detection With Text Mining2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)10.1109/ICORIS50180.2020.9320755(1-6)Online publication date: 27-Oct-2020
https://doi.org/10.1109/ICORIS50180.2020.9320755
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Spread of Hate Speech in Online Social Media

A Measurement Study of Hate Speech in Social Media

Identifying and Categorising Profane Words in Hate Speech