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Relevant bibliographies by topics / Hoax news detection

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Academic literature on the topic 'Hoax news detection'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Hoax news detection"

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Luthfi, Naufal Haritsah, and Agus Hartoyo. "AI Explanation related Covid Hoax Detection Using Support Vector Machine and Logistics Regression Methods." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 170. http://dx.doi.org/10.30865/mib.v7i1.5386.

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Hoax news about Covid is still circulating in society. Especially on social media, this phenomenon still occurs. The existence of this disinformation can cause divisions between communities. Currently, technology can classify hoax news and non-hoax news. But no system can see the reasons for a model to classify hoax news and non-hoax news. Therefore, in this study, a system was developed that can see words on a system that detects hoax and non-hoax news using the Support Vector Machine and Logistic Regression methods. Meanwhile, the Explainable AI method is Local Interpretable Model-agnostic E

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Jocelynne, Charlotte, IGN Lanang Wijayakusuma, and Luh Putu Ida Harini. "Detection of Political Hoax News Using Fine-Tuning IndoBERT." Journal of Applied Informatics and Computing 9, no. 2 (2025): 354–60. https://doi.org/10.30871/jaic.v9i2.8989.

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Indonesia has experienced a surge in the spread of political hoax news, posing a potential threat to democratic and social stability. This study aims to develop a model for detecting political hoax news in the Indonesian language using IndoBERT, a language model optimized for Indonesian text. The dataset was sourced from Kaggle and comprises 20,928 factual news articles and 2,251 hoax news articles from major Indonesian media outlets, including CNN, Kompas, Tempo, and Turnbackhoax. The imbalance between factual and hoax news articles was addressed through undersampling, resulting in 1,302 samp

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Kumar, Guntha Venkata Dhanush, Mamatha V. Jadhav, Anvesh Tadisetti, and Kir an. "A Deep Model on Hoax Detection Using Feed Forward Neural Network and LSTM." Webology 17, no. 2 (2020): 652–62. http://dx.doi.org/10.14704/web/v17i2/web17058.

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The topic of hoax news detection on social media has recently pulled in enormous consideration. Social media not taking any credibility for the news being spread in it makes it more difficult to contain the hoax news. The essential counter measure of comparing websites against a list of labeled hoax news sources is inflexible, and so a machine learning approach is desirable. Our project aims to use Neural Networks to detect hoax news directly, based on the text content of news articles. The model concentrates on discovering hoax news origins, based on the many articles originating from it. Whe

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Sibaroni, Yuliant, Shuhaimi Mahadzir, Sri Suryani Prasetiyowati, and Aditya Firman Ihsan. "Combating Misinformation: Leveraging Deep Learning for Hoax Detection in Indonesian Political Social Media." JURNAL INFOTEL 16, no. 2 (2024): 413–26. http://dx.doi.org/10.20895/infotel.v16i2.1139.

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The rampant spread of hoax news in social media, especially in the political domain, poses a significant challenge that requires immediate attention. To address this issue, automatic hoax news detection using machine learning-based artificial intelligence has emerged as a promising approach. With the approaching presidential election in Indonesia in 2024, the need for effective detection methods becomes even more pressing.This research focuses on proposing an efficient deep learning model for detecting political hoax news on Indonesian social media. Word2vec feature representation and three de

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Heru, Agus Santoso, Hari Rachmawanto Eko, Nugraha Adhitya, Aji Nugroho Akbar, Rosal Ignatius Moses Setiadi De, and Suko Basuki Ruri. "Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 799–806. https://doi.org/10.12928/TELKOMNIKA.v18i2.14744.

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Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment

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Shafira, Alifia. "Hoax COVID-19 News Detection Based on Sentiment Analysis in Indonesian using Support Vector Machine (SVM) Method." International Journal on Information and Communication Technology (IJoICT) 8, no. 2 (2023): 66–77. http://dx.doi.org/10.21108/ijoict.v8i2.682.

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The increasing use of technology makes it easier for information media such as news to be disseminated and does not demand possibilities, there is a lot of hoax news spreading. Twitter is one of the media most frequently used by the public to access and disseminate information. This research will focus on detecting Indonesian language COVID-19 news taken from Twitter. Detection of hoax news can be assisted by using sentiment analysis, one of the uses of classification text. Support Vector Machine (SVM) can be used to perform sentiment analysis tasks. After getting the sentiment analysis result

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Safira, Rachelita Embun, and Akhsin Nurlayli. "Comparative analysis of Indonesian news validity detection accuracy using machine learning." Journal of Engineering and Applied Technology 4, no. 1 (2023): 40–51. http://dx.doi.org/10.21831/jeatech.v4i1.58791.

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Hoax news prediction is required to anticipate the growth of hoax news in social media. This study aimed to determine the best model for predicting whether the news is a hoax or valid based on the dataset taken from Kaggle.com. This study used several data prediction methods: Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naïve Bayes. After the research processes and data testing, the results showed that the best model for predicting hoax news was SVM, which had the highest accuracy, precision, and recall score of the others.

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Saraswati, Ni Wayan Sumartini, I. Putu Krisna Suarendra Putra, I. Dewa Made Krishna Muku, and Gede Dana Pramitha. "Support Vector Machine For Hoax Detection." SINTECH (Science and Information Technology) Journal 6, no. 2 (2023): 107–17. http://dx.doi.org/10.31598/sintechjournal.v6i2.1366.

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Along with the development of information technology, news media has also developed by presenting information online Along with the rapid development of online news, the spread of fake news information (hoaxes) is also increasing rapidly and widely. Hoax news is often spread intentionally for various purposes. Generally, hoax news aims to direct the reader's perception to believe in a bad perception of an event, character or even a company. The motivation is to invite readers to believe something that is not true with the aim of benefiting the news disseminator is something dangerous. This res

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Febriyanty, Nur Elyta, M. Amin Hariyadi, and Cahyo Crysdian. "Hoax Detection News Using Naïve Bayes and Support Vector Machine Algorithm." International Journal of Advances in Data and Information Systems 4, no. 2 (2023): 191–200. http://dx.doi.org/10.25008/ijadis.v4i2.1306.

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Websites and blogs are well-known as media for broadcasting news in various fields such as broadcasting news. The validity of news articles can be valid or fake. Fake news is also known as hoax news. The purpose of making hoax news is to persuade, manipulate, and influence news readers to do things that contradict or prevent correct action. This study proposes to experiment with the Support Vector Machine and Naïve Bayes classifications to detect hoax news in Indonesian. This study uses a dataset from public data, namely news between valid news and hoaxes. The system can classify online news i

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Hermawan, Aditiya, Lidya Lunardi, Yusuf Kurnia, Benny Daniawan, and Junaedi. "Optimizing Convolutional Neural Networks with Particle Swarm Optimization for Enhanced Hoax News Detection." Journal of Information Systems Engineering and Business Intelligence 11, no. 1 (2025): 53–64. https://doi.org/10.20473/jisebi.11.1.53-64.

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Background: The global spreading of hoax news is causing significant challenges, by misleading the public and undermining public trust in media and institutions. This issue is worsened by the rapid spreading of misinformation which is facilitated by digital platforms, triggering social unrest and threatening national security. To overcome this problem, reliable and robust method is essential to adapt to the evolving tactics of misleading information spreading. Objective: This study aimed to improve the accuracy of hoax news detection tools by evaluating the effectiveness of Deep Learning metho

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Dissertations / Theses on the topic "Hoax news detection"

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Tsurumaru, Hiroshi. "Comprehensive evaluation of oxidative capacity of ambient air with new detection technique of HOx (OH, HO{2}) radical production rate." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/195990.

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Books on the topic "Hoax news detection"

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Tanenbaum, Robert. Hoax: A novel. 4th ed. Atria Books, 2004.

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Tanenbaum, Robert. Hoax: A novel. Atria Books, 2004.

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Handler, David. The man who would be F. Scott Fitzgerald. Doubleday, 1991.

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Handler, David. The Man Who Would Be F. Scott Fitzgerald. I Books, 2002.

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Book chapters on the topic "Hoax news detection"

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Mysore, Naga Raju, K. Sankar, M. Islabudeen, and Sampath AK. "Designing Deep Classifier for Effective Detection of Spoof News in Twitter." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220015.

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A Spoof news is a fraud content meant to misguide the reader about the event with ill motive. In this article a reactive technique using deep learning is proposed to deal with it effectively. Spoof news are innumerable in number over microblog twitter and have wide range of bad effects overall. This is causing chaos and hoax among the readers about the issue. They are getting mislead about the issue a lot. As of now automatic locators of fake news are ineffective and few in number. This emphasized us to come up with smart locator with deep learning mechanism. One way of dealing with this issue

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Conference papers on the topic "Hoax news detection"

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Yakub, Sy Yuliani, David Agustriawan, Irmawati, et al. "Cyber Security for Hoax News Detection with Similarity Algorithm." In 2024 Ninth International Conference on Informatics and Computing (ICIC). IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10956546.

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Laimeheriwa, Jonathan, Irene Anindaputri Iswanto, Wiwi Oktriani, and Muhammad Fadlan Hidayat. "A Survey on Indonesian Hoax Analyzer and Fake News Detection Using Deep Learning Techniques." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903927.

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Assiroj, Priati, Meyliana, Achmad N. Hidayanto, Harjanto Prabowo, and Harco Leslie Hendric Spits Warnars. "Hoax News Detection on Social Media: A Survey." In 2018 Indonesian Association for Pattern Recognition International Conference (INAPR). IEEE, 2018. http://dx.doi.org/10.1109/inapr.2018.8627053.

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Rahmawati, Arnetta, Andry Alamsyah, and Ade Romadhony. "Hoax News Detection Analysis using IndoBERT Deep Learning Methodology." In 2022 10th International Conference on Information and Communication Technology (ICoICT). IEEE, 2022. http://dx.doi.org/10.1109/icoict55009.2022.9914902.

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Rahmat, Muhammad Abdillah, Indrabayu, and Intan Sari Areni. "Hoax Web Detection For News in Bahasa Using Support Vector Machine." In 2019 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2019. http://dx.doi.org/10.1109/icoiact46704.2019.8938425.

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Pratiwi, Inggrid Yanuar Risca, Rosa Andrie Asmara, and Faisal 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). IEEE, 2017. http://dx.doi.org/10.1109/icts.2017.8265649.

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Pranadia, Aldi, and Rila Mandala. "Implementation of Query Expansion to Enhance Word2Vec Performance in Hoax News Detection Systems." In 2023 10th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA). IEEE, 2023. http://dx.doi.org/10.1109/icaicta59291.2023.10389877.

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Prasetijo, Agung B., R. Rizal Isnanto, Dania Eridani, Yosua Alvin Adi Soetrisno, M. Arfan, and Aghus Sofwan. "Hoax detection system on Indonesian news sites based on text classification using SVM and SGD." In 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). IEEE, 2017. http://dx.doi.org/10.1109/icitacee.2017.8257673.

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Imanuel, Jason, Lusia Kintanswari, Vincent, Henry Lucky, and Andry Chowanda. "Explainable Artificial Intelligence (XAI) on Hoax Detection Using Decision Tree C4.5 Method for Indonesian News Platform." In 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2022. http://dx.doi.org/10.1109/icsintesa56431.2022.10041567.

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Asta, Reyhan Septri, and Erwin Budi Setiawan. "Fake News (Hoax) Detection on Social Media Using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) Methods." In 2023 11th International Conference on Information and Communication Technology (ICoICT). IEEE, 2023. http://dx.doi.org/10.1109/icoict58202.2023.10262617.

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