Analysis of Hate Speech in 2024 Elections on Social Media Platforms Using Natural Language Processing (NLP) Methods

Authors

  • Richardo Johan Tanujaya Universitas Pelita Harapan Medan
  • Farhan Mohammed Universitas Pelita Harapan Medan
  • Gabriela Callista Halim Universitas Pelita Harapan Medan
  • Cindy Thalia Universitas Pelita Harapan Medan
  • Shane Michael Colyn Universitas Pelita Harapan Medan

DOI:

https://doi.org/10.55123/ijisit.v1i2.5

Keywords:

Hate Speech Detection, NLP, Lexicon, Naive Bayes, Bi - LSTM

Abstract

The surge in digital activity during the 2024 General Election in Indonesia has triggered the spread of hate speech on social media, potentially disrupting democratic stability. This study aims to analyze the effectiveness of hate speech detection on the X platform using Natural Language Processing (NLP) methods. The technical approach combines Regular Expressions for data pre-processing and a Lexicon-Based method to identify offensive words based on a predefined dictionary. Data collection was conducted using the Uninvolved Conversation Observation Technique (TSBLC) with a total of 100 election-related comment samples. Test results indicate that this rule-based program is capable of detecting the presence of hate speech within the data samples. However, comparative analysis reveals that the lexicon method yields lower accuracy compared to advanced Machine Learning models, such as Naive Bayes (93%) and Bi-LSTM (96.9%), utilized in previous studies. In conclusion, while the lexicon approach offers structured basic detection, the integration of machine learning models is highly recommended to enhance accuracy and achieve deeper contextual understanding in future research.

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References

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Published

2025-12-25

How to Cite

Richardo Johan Tanujaya, Farhan Mohammed, Gabriela Callista Halim, Cindy Thalia, & Shane Michael Colyn. (2025). Analysis of Hate Speech in 2024 Elections on Social Media Platforms Using Natural Language Processing (NLP) Methods. IJISIT: International Journal of Computer Science and Information Technology, 1(2), 73–80. https://doi.org/10.55123/ijisit.v1i2.5

Issue

Vol. 1 No. 2 (2024): December 2024

Section

Articles

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Copyright (c) 2024 Richardo Johan Tanujaya, Farhan Mohammed, Gabriela Callista Halim, Cindy Thalia, Shane Michael Colyn

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