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Cited by 19Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningText and Document Classification Technologies

Optimizing Deep Learning for Detection Cyberbullying Text in Indonesian Language

Anindyati, Laksmi, Purwarianti, Ayu, Nursanti, Ade·2019·2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)
DOI 10.1109/icaicta.2019.8904108
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Abstract

Cyberbullying in Indonesia currently become a concern due to the increasing usage of social media. Cyberbullying detection is an important step to make good environments in social media interactions. This research is part of computational linguistics that focuses on the usage of deep learning to detect bullying sentence on Twitter. There are two important processes in this study. First, the process of forming a word representation. Second, the classification process for detecting bullying sentences. Pre-trained process to build the new representation of term/word is performed independently. Word2vec is used as a tool for the pre-trained process. There are two types of data used in the pre-training process. The first type of data only used testing data and training data, while the second type of data is the overall data, total 26,800 unique Twitter sentences including test data and training data. The classification process is formed using three main algorithms that are popular for text classification: LSTM, bi-LSTM, and CNN. 9.854 labeled sentences are extracted from 2.584 Twitter conversations used as the dataset. The dataset consists of 1.680 sentences are labeled as a bully and 6.343 sentences are labeled as neutral. A total of 504 experiments are conducted in this research by exploiting the preprocessing stage for determining machine learning features, dropout layers configuration and the algorithm of deep learning. The experiments show that the accuracy score reaches 90.57% while the recall score for bully class reaches 75.7%.

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Crossref · 10.1109/icaicta.2019.8904108

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  • Several studies have been conducted on automatic identification of cyberbullying using different independent ML techniques.

    “Several studies [21,64,115,134] have been conducted on the automatic identification of cyberbullying by using different independent ML techniques.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [64]

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  • A Review on Deep-Learning-Based Cyberbullying Detection

    Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103 · see citing works

    “…Twitter (combination of 3 datasets) [115,190,192] Bi-GRU, Transformer Block, and CNN Detecting Aggressive Behavior Twitter (Indonesian Language) [64] LSTM, Bi-LSTM, and CNN Cyberbullying Detection YouTube [193] Bi-LSTM with attention Cyberbullying Detection [25] Bangla and Romanized Bangla [18]…”
    BackgroundMethodInfluential

Related

Authors
Anindyati, L., Purwarianti, A., & Nursanti, A.
Affiliations
  • Bandung Institute of Technology (ID)
  • Yarsi University (ID)
ORCID
Purwarianti, Ayu
Venue
2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)
Publisher
IEEE
Published
2019-09
Pages
1-5
DOI
10.1109/icaicta.2019.8904108
OpenAlex
W3015877763
Cited by
19
References
11
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 8 · 2022: 3 · 2021: 2 · 2020: 2
FWCI
0.67
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Keywords
cyberbullying detection, Indonesian language, Twitter, text classification, Word2Vec, word representation, pre-trained word embeddings, LSTM, BiLSTM, convolutional neural networks, social media

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