A Highly Accurate Internet-Based Fake Information Detection Tool for Indonesian Twitter

Rizal Arifin, Gus Nanang Syaifuddiin, Desriyanti Desriyanti, Zulkham Umar Rosyidin, Ghulam Asrofi Buntoro


The dissemination of fake information through social media has several harmful effects on the social life of a nation. Indonesia has been afflicted by the dissemination of erroneous information regarding the negative health consequences of vaccination, resulting in widespread unwillingness to undergo immunization. Therefore, it is necessary to combat such misleading information. We developed a web application using machine learning technologies to identify bogus information flowing on Indonesian Twitter. A Passive-Aggressive Classifier and n-gram tokenization are used to handle data. The application test results indicate that the detection accuracy, precision, and recall for 1-3 grams of tokenization are higher than 90%. In addition, the black box approach yields reliable findings for all application functionalities.

Full Text:



V. L. Muzykant, M. A. Muqsith, R. R. Pratomo, and V. Barabash, “Fake News on COVID-19 in Indonesia,” in Pandemic Communication and Resilience. Risk, Systems and Decisions, D. M. Berube, Ed. Cham: Springer, 2021, pp. 363–378.

G. A. Buntoro, R. Arifin, G. N. Syaifuddiin, A. Selamat, O. Krejcar, and H. Fujita, “The implementation of the machine learning algorithm for the sentiment analysis of Indonesia’s 2019 presidential election,” IIUM Eng. J., vol. 22, no. 1, pp. 78–92, 2021, doi: https://doi.org/10.31436/iiumej.v22i1.1532.

T. Buchanan, “Why do people spread false information online? The effects of message and viewer characteristics on self-reported likelihood of sharing social media disinformation,” PLoS One, vol. 15, no. 10, p. e0239666, 2020, doi: https://doi.org/10.1371/journal.pone.0239666.

M. Celliers and M. Hattingh, “A Systematic Review on Fake News Themes Reported in Literature,” in Responsible Design, Implementation and Use of Information and Communication Technology, 2020, pp. 223–234, doi: https://doi.org/10.1007/978-3-030-45002-1_19.

T. Khan, A. Michalas, and A. Akhunzada, “Fake news outbreak 2021: Can we stop the viral spread?,” J. Netw. Comput. Appl., vol. 190, p. 103112, 2021, doi: https://doi.org/10.1016/j.jnca.2021.103112.

A. Alasmari, A. Addawood, M. Nouh, W. Rayes, and A. Al-Wabil, “A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia,” Futur. Internet, vol. 13, no. 10, p. 254, 2021, doi: https://doi.org/10.3390/fi13100254.

M. Montesi, “Understanding fake news during the Covid-19 health crisis from the perspective of information behaviour: The case of Spain,” J. Librariansh. Inf. Sci., vol. 53, no. 3, pp. 454–465, 2020, doi: https://doi.org/10.1177/0961000620949653.

S. van der Linden, J. Roozenbeek, and J. Compton, “Inoculating Against Fake News About COVID-19,” Front. Psychol., vol. 11, p. 566790, 2020, doi: https://doi.org/10.3389/fpsyg.2020.566790.

K. Lutfiyah, “Hoax and Fake News During Covid-19: Is the Law Effective in Overcoming It?,” Indones. J. Int’l Clin. Leg. Educ., vol. 2, no. 3, pp. 345–360, 2020, doi: https://doi.org/10.15294/ijicle.v2i3.38422.

N. M. Nasir, B. Baequni, and M. I. Nurmansyah, “Misinformation Related to Covid-19 in Indonesia,” J. Adm. Kesehat. Indones., vol. 8, no. 1, pp. 51–59, 2020, doi: http://dx.doi.org/10.20473/jaki.v8i0.2020.51-59.

M. Rasidin, D. Witro, B. Yanti, R. Purwaningsih, and W. Nurasih, “The Role of Government in Preventing The Spread if Hoax Related The 2019 Elections in Social Media,” Diakom, vol. 3, no. 2, pp. 127–3, 2020, doi: https://doi.org/10.17933/diakom.v3i2.76.

Y. I. Ferdiawan, P. A. D. Nurjanah, E. P. Krisdyan, A. Hidayatullah, H. J. M. Sirait, and N. A. Rakhmawati, “HOAX Impact to Community Through Social Media Indonesia,” Cakrawala, vol. 19, no. 1, pp. 121–124, 2019, doi: https://doi.org/10.31294/jc.v19i1.4452.

M. J. Hasan, A. Rai, Z. Ahmad, and J.-M. Kim, “A Fault Diagnosis Framework for Centrifugal Pumps by Scalogram-Based Imaging and Deep Learning,” IEEE Access, vol. 9, pp. 58052–58066, 2021, doi: https://doi.org/10.1109/CEEICT.2016.7873115.

M. J. Hasan, D. Shon, K. Im, H.-K. Choi, D.-S. Yoo, and J.-M. Kim, “Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals,” Appl. Sci., vol. 10, no. 21, p. 7639, 2020, doi: https://doi.org/10.3390/app10217639.

M. J. Hasan, J. Uddin, and S. N. Pinku, “A novel modified SFTA approach for feature extraction,” in 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2016, pp. 1–5, doi: https://doi.org/10.1109/CEEICT.2016.7873115.

H. E. Wynne and Z. Z. Wint, “Content Based Fake News Detection Using N-Gram Models,” in Information Integration and Web-based Applications & Services, 2019, pp. 669–673, doi: https://doi.org/10.1145/3366030.3366116.

H. Ahmed, I. Traore, and S. Saad, “Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques,” in Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, 2017, pp. 127–138, doi: https://doi.org/10.1007/978-3-319-69155-8_9.

H. Ahmed, I. Traore, and S. Saad, “Detecting opinion spams and fake news using text classification,” Secur. Priv., vol. 1, p. e9, 2018, doi: https://doi.org/10.1002/spy2.9.

J. Huang, “Detecting Fake News With Machine Learning,” J. Phys. Conf. Ser., vol. 1693, p. 012158, 2020, doi: https://doi.org/10.1088/1742-6596/1693/1/012158.

M. J. Awan et al., “Fake News Data Exploration and Analytics,” Electronics, vol. 10, p. 2326, 2021, doi: https://doi.org/10.3390/electronics10192326.

R. R. Mandical, N. Mamatha, N. Shivakumar, R. Monica, and A. N. Krishna, “Identification of Fake News Using Machine Learning,” in 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2020, pp. 1–6, doi: https://doi.org/10.1109/CONECCT50063.2020.9198610.

A. Chugh, Y. Arora, J. Singh, Shobhit, and Ronak, “Media Manipulation Detection System Using Passive Aggressive,” Int. J. Innov. Res. Comput. Sci. Technol., vol. 9, no. 3, pp. 48–52, 2021, doi: https://doi.org/10.21276/ijircst.2021.9.3.8.

S. Gupta and P. Meel, “Fake News Detection Using Passive-Aggressive Classifier,” in Inventive Communication and Computational Technologies, 2020, pp. 155–164, doi: https://doi.org/10.1007/978-981-15-7345-3_13.

S. K. Akpatsa, H. Lei, X. Li, and V.-H. K. S. Obeng, “Evaluating Public Sentiments of Covid-19 Vaccine Tweets Using Machine Learning Techniques,” J. Inf. Syst. Telecommun., vol. 46, no. 1, pp. 69–75, 2022, doi: https://doi.org/10.31449/inf.v46i1.3483.

G. N. Syaifuddiin et al., “Hoax Identification of Indonesian Tweeters Using Ensemble Classifier,” J. Inf. Syst. Telecommun., vol. IN PRESS, 2022.

B. P. Pratama, I. B. V. Ristianto, I. A. Prayogo, Nasrullah, and A. Saifudin, “Pengujian perangkat lunak sistem informasi penilaian mahasiswa dengan teknik boundary value analysis menggunakan metode black box testing,” J. Artif. Intell. Innov. Appl., vol. 1, no. 1, pp. 32–36, 2020.

B. Zaman, A. Justitia, K. N. Sani, and E. Purwanti, “An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm,” Cybern. Inf. Technol., vol. 20, no. 1, pp. 82–94, 2020, doi: https://doi.org/10.2478/cait-2020-0006.

I. Y. R. Pratiwi, R. A. Asmara, and F. Rahutomo, “Study of hoax news detection using naïve bayes classifier in Indonesian language,” in 2017 11th International Conference on Information Communication Technology and System (ICTS), 2017, pp. 73–78, doi: https://doi.org/10.1109/ICTS.2017.8265649.

DOI: https://doi.org/10.31449/inf.v46i9.4416

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.