Survei Literatur: Deteksi Berita Palsu Menggunakan Pendekatan Deep Learning

  • Rio Yunanto Universitas Komputer Indonesia
  • Apriani Puti Purfini Universitas Komputer Indonesia
  • Angga Prabuwisesa Universitas Indraprasta PGRI
Keywords: Article, Lie, Classification, Algorithm, Social Media

Abstract

Social media has become an inseparable part of everyday life in modern society to make it easier to interact and communicate with each other. The purpose of this study is to review and compare the deep learning methods implemented in the case of detecting fake news from several previous studies, and to get an overview of the corpus or dataset used by previous studies. This research is also to help researchers identify and map the use of deep learning algorithms in cases of detecting fake news. The research method is conducting a literature survey of 12 literatures obtained from the ScienceDirect and IEEE Xplore websites. The collection of literature that has been surveyed is selected based on the year published in 2021 with the topic of research on detection of fake news using a deep learning approach. The results of this study summarize that the strategy to detect fake news can be done with four approaches, based on the content, based on the writing style, based on the distribution pattern, and based on the credibility of the source. The results of this research also show that the Convolutional Neural Network algorithm is a favorite of researchers by appearing 6 times in the literature collection. The next favorite algorithm is Long Short Term Memory which appears in 5 literatures and Bidirectional LSTM which appears in 4 literatures.

References

C. Reuter and M. A. Kaufhold, “Fifteen years of social media in emergencies: A retrospective review and future directions for crisis Informatics,” J. Contingencies Cris. Manag., vol. 26, no. 1, 2018, doi: 10.1111/1468-5973.12196.

N. Hidaya, N. Qalby, S. S. Alaydrus, A. Darmayanti, and A. P. Salsabila, “Pengaruh Media Sosial Terhadap Penyebaran Hoax Oleh Digital Native,” Makassar, 2019.

X. Cao and L. Yu, “Exploring The Influence Of Excessive Social Media Use At Work: A Three-Dimension Usage Perspective,” Int. J. Inf. Manage., vol. 46, no. 1, pp. 83–92, 2019.

A. R. Ahmad and H. R. Murad, “The Impact of Social Media on Panic During The COVID-19 Pandemic in Iraqi Kurdistan: Online Questionnaire Study,” J. Med. Internet Res., vol. 22, no. 5, 2020.

M. R. Ramadhani and A. R. I. Pratama, “Analisis Kesadaran Cyber Security Pada Pengguna Media Sosial Di Indonesia,” AUTOMATA, vol. 1, no. 2, 2020, [Online]. Available: https://journal.uii.ac.id/AUTOMATA/article/download/15426/10219.

S. Gerintya, “Hoaks dan Bahaya Rendahnya Kepercayaan Terhadap Media,” 2018. https://tirto.id/hoaks-dan-bahaya-rendahnya-kepercayaan-terhadap-media-cKAx (accessed Jul. 28, 2021).

E. Lararenjana, “Mengenal Arti Hoax Atau Berita Bohong, Ketahui Jenis dan Ciri-Cirinya,” 2020. https://www.merdeka.com/jatim/mengenal-arti-hoax-atau-berita-bohong-dan-cara-tepat-menyikapinya-kln.html?page (accessed Jul. 28, 2021).

X. Zhou and R. Zafarani, “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities,” ACM Comput. Surv., vol. 53, no. 5, pp. 1–40, Oct. 2020, doi: 10.1145/3395046.

C. B. C. Latha and S. C. Jeeva, “Improving The Accuracy Of Prediction Of Heart Disease Risk Based On Ensemble Classification Techniques,” Informatics Med. Unlocked, vol. 16, 2019, doi: 10.1016/j.imu.2019.100203.

A. Yanuar, “Pengenalan Deep Learning,” ugm.ac.id, 2018. https://machinelearning.mipa.ugm.ac.id/2018/06/10/pengenalan-deep-learning (accessed Jul. 28, 2021).

A. Kamilaris and F. X. Prenafeta-Boldú, “Deep Learning In Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147. 2018, doi: 10.1016/j.compag.2018.02.016.

C. Fisch and J. Block, “Six tips for your (systematic) literature review in business and management research,” Manag. Rev. Q., vol. 68, no. 2, pp. 103–106, 2018, doi: 10.1007/s11301-018-0142-x.

R. Asyik, “Inilah Beda Misinformasi, Disinformasi, dan Malinformasi,” ayobandung.com, 2019. https://ayobandung.com/read/2019/02/01/44283/inilah-beda-misinformasi-disinformasi-dan-malinformasi.

P. Meel and D. K. Vishwakarma, “A Temporal Ensembling Based Semi-Supervised Convnet for The Detection of Fake News Articles,” Expert Syst. Appl., vol. 177, p. 115002, Sep. 2021, doi: 10.1016/j.eswa.2021.115002.

T. E. Trueman, A. K. J., N. P., and V. J., “Attention-based C-BiLSTM for Fake News Detection,” Appl. Soft Comput., vol. 110, p. 107600, Oct. 2021, doi: 10.1016/j.asoc.2021.107600.

M. Choudhary, S. S. Chouhan, E. S. Pilli, and S. K. Vipparthi, “BerConvoNet: A Deep Learning Framework for Fake News Classification,” Appl. Soft Comput., vol. 110, p. 107614, Oct. 2021, doi: 10.1016/j.asoc.2021.107614.

S. A. Alameri and M. Mohd, “Comparison of Fake News Detection using Machine Learning and Deep Learning Techniques,” in 2021 3rd International Cyber Resilience Conference (CRC), Jan. 2021, pp. 1–6, doi: 10.1109/CRC50527.2021.9392458.

M. H. Goldani, R. Safabakhsh, and S. Momtazi, “Convolutional Neural Network with Margin Loss for Fake News Detection,” Inf. Process. Manag., vol. 58, no. 1, p. 102418, Jan. 2021, doi: 10.1016/j.ipm.2020.102418.

S. Sridhar and S. Sanagavarapu, “Fake News Detection and Analysis using Multitask Learning with BiLSTM CapsNet Model,” in 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Jan. 2021, pp. 905–911, doi: 10.1109/Confluence51648.2021.9377080.

K. Ivancova, M. Sarnovski, and V. Maslej-Krcsnakova, “Fake News Detection in Slovak Language using Deep Learning Techniques,” in 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI), Jan. 2021, pp. 000255–000260, doi: 10.1109/SAMI50585.2021.9378650.

J. A. Nasir, O. S. Khan, and I. Varlamis, “Fake News Detection: A Hybrid CNN-RNN Based Deep Learning Approach,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100007, Apr. 2021, doi: 10.1016/J.JJIMEI.2020.100007.

X. Zhi et al., “Financial Fake News Detection with Multi fact CNN-LSTM Model,” in 2021 IEEE 4th International Conference on Electronics Technology (ICET), May 2021, pp. 1338–1341, doi: 10.1109/ICET51757.2021.9450924.

B. P. Nayoga, R. Adipradana, R. Suryadi, and D. Suhartono, “Hoax Analyzer for Indonesian News Using Deep Learning Models,” Procedia Comput. Sci., vol. 179, pp. 704–712, 2021, doi: 10.1016/j.procs.2021.01.059.

H. Yuan, J. Zheng, Q. Ye, Y. Qian, and Y. Zhang, “Improving Fake News Detection with Domain-Adversarial and Graph-attention Neural Network,” Decis. Support Syst., p. 113633, Jul. 2021, doi: 10.1016/j.dss.2021.113633.

S. R. Sahoo and B. B. Gupta, “Multiple Features Based Approach for Automatic Fake News Detection on Social Networks using Deep Learning,” Appl. Soft Comput., vol. 100, 2021, doi: 10.1016/j.asoc.2020.106983.

A. Bondielli and F. Marcelloni, “A survey on fake news and rumour detection techniques,” Inf. Sci. (Ny)., vol. 497, 2019, doi: 10.1016/j.ins.2019.05.035.

J. C. S. Reis, A. Correia, F. Murai, A. Veloso, F. Benevenuto, and E. Cambria, “Supervised Learning for Fake News Detection,” IEEE Intell. Syst., vol. 34, no. 2, pp. 76–81, Mar. 2019, doi: 10.1109/MIS.2019.2899143.

M. P. Utami, O. D. Nurhayati, and B. Warsito, “Hoax Information Detection System Using Apriori Algorithm and Random Forest Algorithm in Twitter,” 2020, doi: 10.1109/ICIDM51048.2020.9339648.

A. Benamira, B. Devillers, E. Lesot, A. K. Ray, M. Saadi, and F. D. Malliaros, “Semi-supervised learning and graph neural networks for fake news detection,” 2019, doi: 10.1145/3341161.3342958.

H. K. Farid, E. B. Setiawan, and I. Kurniawan, “Implementation Information Gain Feature Selection for Hoax News Detection on Twitter using Convolutional Neural Network (CNN),” Indones. J. Comput., vol. 5, no. 3, pp. 23–36, 2020.

A. A. Kurniawan and M. Mustikasari, “Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia,” J. Inform. Univ. Pamulang, vol. 5, no. 4, pp. 544–552, 2021.

Published
2021-09-06
How to Cite
[1]
R. Yunanto, A. Purfini, and A. Prabuwisesa, “Survei Literatur: Deteksi Berita Palsu Menggunakan Pendekatan Deep Learning”, JAMIKA, vol. 11, no. 2, pp. 118-130, Sep. 2021.