Detection of Radicalism Speech on Indonesian Tweet Using Convolutional Neural Network
-
Faiza Aulia Rahma Putra
*
Telkom University, Bandung,
Indonesia
- Yuliant Sibaroni Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
The ease of disseminating information today is inseparable from the rapid development of information technology. Unfortunately, radical groups also use this condition to spread propaganda and recruit members through social media such as Facebook and Twitter. Therefore, detecting radicalism on social media is essential, given the ease with which information can be spread that can affect social media users. Several studies to classify radicalism speech have been carried out using machine learning algorithms such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). However, only a few used the Indonesian language and even utilized a small dataset. This study proposed to detect radicalism speech in Indonesian tweets using Convolutional Neural Network (CNN) and Word2Vec as feature extraction. The dataset is a collection of Indonesian-language tweets obtained through tweet crawling. CNN modeling was conducted using several scenarios with the number of filter parameter values = 100 and 300, and kernel size parameter value = 3, 5, 7, 9. From the training process using the scenarios above, the most optimal model is obtained with parameter filters = 300 and kernel size = 7, producing the best accuracy of 87.87% and average accuracy of 86.93%. Based on the best model obtained, an evaluation was carried out on the test data, which resulted in an accuracy of 87.15%.
Downloads
References
A. Rekik, S. Jamoussi, and A. ben Hamadou, “Violent Vocabulary Extraction Methodology: Application to the Radicalism Detection on Social Media,” in Computational Collective Intelligence, 2019, pp. 97–109. doi: https://doi.org/10.1007/978-3-030-28374-2_9.
M. Nouh, J. R. C. Nurse, and M. Goldsmith, “Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter,” in 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), 2019, pp. 98–103. doi: 10.1109/ISI.2019.8823548.
S. Aldera, A. Emam, M. Al-Qurishi, M. Alrubaian, and A. Alothaim, “Online Extremism Detection in Textual Content: A Systematic Literature Review,” IEEE Access, vol. 9, pp. 42384–42396, 2021, doi: 10.1109/ACCESS.2021.3064178.
M. Fernandez, M. Asif, and H. Alani, “Understanding the Roots of Radicalisation on Twitter,” in WebSci 2018 - Proceedings of the 10th ACM Conference on Web Science, May 2018, pp. 1–10. doi: 10.1145/3201064.3201082.
I. Nur, A. H. Nawawie, H. Fajarwati, and H. Chusna, “Embracing Radicalism and Extremism in Indonesia with the Beauty of Islam,” Asian Research Journal of Arts & Social Sciences, pp. 1–18, Jan. 2020, doi: 10.9734/arjass/2020/v10i230141.
V. K. Vijayan, K. R. Bindu, and L. Parameswaran, “A comprehensive study of text classification algorithms,” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 1109–1113. doi: 10.1109/ICACCI.2017.8125990.
J. Teuwen and N. Moriakov, “Convolutional neural networks,” in Handbook of Medical Image Computing and Computer Assisted Intervention, Elsevier, 2019, pp. 481–501. doi: 10.1016/B978-0-12-816176-0.00025-9.
A. Jacovi, O. S. Shalom, and Y. Goldberg, “Understanding Convolutional Neural Networks for Text Classification,” Sep. 2018, [Online]. Available: http://arxiv.org/abs/1809.08037
Z. Wang and Z. Qu, “Research on Web text classification algorithm based on improved CNN and SVM,” in 2017 IEEE 17th International Conference on Communication Technology (ICCT), 2017, pp. 1958–1961. doi: 10.1109/ICCT.2017.8359971.
Muh. Subhan, A. Sudarsono, and A. Barakbah, “Preprocessing of Radicalism Dataset to Predict Radical Content in Indonesia,” in 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 2017, pp. 270–275. doi: 10.1109/KCIC.2017.8228598.
E. Miranda, M. Aryuni, Y. Fernando, and T. M. Kibtiah, “A Study of Radicalism Contents Detection in Twitter: Insights From Support Vector Machine Technique,” in 2020 International Conference on Information Management and Technology (ICIMTech), 2020, pp. 549–554. doi: 10.1109/ICIMTech50083.2020.9211229.
S. Ahmad, M. Z. Asghar, F. M. Alotaibi, and I. Awan, “Detection and classification of social media-based extremist affiliations using sentiment analysis techniques,” Human-centric Computing and Information Sciences, vol. 9, no. 1, p. 24, 2019, doi: 10.1186/s13673-019-0185-6.
D. A. N. Taradhita and I. K. G. D. Putra, “Hate Speech Classification in Indonesian Language Tweets by Using Convolutional Neural Network,” Journal of ICT Research and Applications, vol. 14, no. 3, pp. 225–239, Feb. 2021, doi: 10.5614/itbj.ict.res.appl.2021.14.3.2.
F. Enríquez, J. A. Troyano, and T. López-Solaz, “An approach to the use of word embeddings in an opinion classification task,” Expert Systems with Applications, vol. 66, pp. 1–6, 2016, doi: https://doi.org/10.1016/j.eswa.2016.09.005.
S. Qaiser and R. Ali, “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents,” International Journal of Computer Applications, vol. 181, Jul. 2018, doi: 10.5120/ijca2018917395.
Y. Kim, “Convolutional Neural Networks for Sentence Classification,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Oct. 2014, pp. 1746–1751. doi: 10.3115/v1/D14-1181.
A. Aghnia and I. Sandy, “Kriteria Situs Islam Radikal Versi BNPT,” CNN Indonesia, Apr. 01, 2015. https://www.cnnindonesia.com/teknologi/20150401093434-185-43429/kriteria-situs-islam-radikal-versi-bnpt (accessed Jan. 18, 2022).
T. Adewumi, F. Liwicki, and M. Liwicki, “Word2Vec: Optimal hyperparameters and their impact on natural language processing downstream tasks,” Open Computer Science, vol. 12, no. 1, pp. 134–141, Jan. 2022, doi: 10.1515/comp-2022-0236.
W. Ling, C. Dyer, A. W. Black, and I. Trancoso, “Two/Too Simple Adaptations of Word2Vec for Syntax Problems,” in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, May 2015, pp. 1299–1304. doi: 10.3115/v1/N15-1142.
B. Jang, I. Kim, and J. W. Kim, “Word2vec convolutional neural networks for classification of news articles and tweets,” PLoS ONE, vol. 14, no. 8, Aug. 2019, doi: 10.1371/journal.pone.0220976.
Y. Zhang and B. Wallace, “A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification,” in Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Nov. 2017, pp. 253–263. [Online]. Available: https://aclanthology.org/I17-1026
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Detection of Radicalism Speech on Indonesian Tweet Using Convolutional Neural Network
Pages: 441−447
Copyright (c) 2022 Faiza Aulia Rahma Putra, Yuliant Sibaroni
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
