A smart System for Fake News Detection Using Machine Learning

Jain, Anjali; Shakya, Avinash; Khatter, Harsh; Gupta, Amit Kumar

doi:10.1109/icict46931.2019.8977659

Cited by 121 publications

(33 citation statements)

References 7 publications

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“…Therefore, machine learning (ML) is one of the potential candidates as a counterstrategy to handle and classify the authenticity of a large volume of information automatically and in real-time. To identify fake news, scientists have developed ML-based false news and misinformation credibility inference models, which form a deep diffusive network model to memorize news articles, writers, and topics [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

et al. 2021

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Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.

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Section: Introductionmentioning

confidence: 99%

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

et al. 2021

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“…Another study (Najar et al 2019) Fake news detection using Bayesian interference used Bag of Words using Multinomial Model (MM), Dirichlet Compound Multinomial (DCM) and Deterministic Annealing Expectation-Maximization(EDCM-DAEM) and EDCM-Bayesian, EDCM-Bayesian better accuracy than other classi ers, classi cation accuracy 87.85 on BS-Detector dataset. In study (Jain et al 2019;Reis et al 2019) used different textual features like language features (syntax) such as n-gram and part of speech tagging, lexical features (character and word-level signals), psycholinguistic features, semantic features and subjectivity and sentiment scores of a text using classi cation of K-Nearest neighbors (KNN), Naïve Bayes(NB), Random forests(RF), Support Vector Machine (SVM) with RBF kernel (SVM), and XGBoost (XGB). Random forest and XGB performed best using handcraft features, web-based networking media.…”

Section: Related Workmentioning

confidence: 93%

Machine learning for fake news classification with optimal feature selection

et al. 2022

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Nowadays, information is published in newspapers and social media while transmitted on radio and television about current events and speci c elds of interest nationwide and abroad. It becomes di cult to explicit what is real and what is fake due to the explosive growth of online content. As a result, fake news has become epidemic and immensely challenging to analyze fake news to be veri ed by the producers in the form of data process outlets not to mislead the people. Indeed, it is a big challenge to the government and public to debate the situation depending on case to case. For the purpose several websites were developed for this purpose to classify the news as either real or fake depending on the website logic and algorithm. A mechanism has to be taken on fact-checking rumors and statements, particularly those that get thousands of views and likes before being debunked and refuted by expert sources. Various machine learning techniques have been used to detect and correctly classi ed of fake news. However, these approaches are restricted in terms of accuracy. This study has applied a Random Forest (RF) classi er to predict fake or real news. For this prpose, twenty-three (23) textual features are extracted from ISOT Fake News Dataset. Four best feature selection techniques like Chi2, Univariate, information gain and Feature importance are used for selecting fourteen best features out of twentythree. The proposed model and other benchmark techniques are evaluated on the dataset by using best features. Experimental ndings show that, the proposed model outperformed state-of-the-art machine learning techniques such as GBM, XGBoost and Ada Boost Regression Model in terms of classi cation accuracy.

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“…Then, they suggested that meta-data and additional information can be utilized to improve the robustness and performance. Jain et al [21] proposed a mix of Naïve Bayes classifier, SVM, and natural language processing techniques on a fake news dataset and their model accuracy is up to 93.6% which was better than the baseline results by 6.85%. Reis et al [35] provided a great understanding of how features are used in the decisions taken by models.…”

Section: Literature Reviewmentioning

confidence: 98%

A Novel Stacking Approach for Accurate Detection of Fake News

et al. 2021

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With the increasing popularity of social media, people has changed the way they access news. News online has become the major source of information for people. However, much information appearing on the Internet is dubious and even intended to mislead. Some fake news are so similar to the real ones that it is difficult for human to identify them. Therefore, automated fake news detection tools like machine learning and deep learning models have become an essential requirement. In this paper, we evaluated the performance of five machine learning models and three deep learning models on two fake and real news datasets of different size with hold out cross validation. We also used term frequency, term frequencyinverse document frequency and embedding techniques to obtain text representation for machine learning and deep learning models respectively. To evaluate models' performance, we used accuracy, precision, recall and F1-score as the evaluation metrics and a corrected version of McNemar's test to determine if models' performance is significantly different. Then, we proposed our novel stacking model which achieved testing accuracy of 99.94% and 96.05 % respectively on the ISOT dataset and KDnugget dataset. Furthermore, the performance of our proposed method is high as compared to baseline methods. Thus, we highly recommend it for fake news detection.

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A smart System for Fake News Detection Using Machine Learning

Cited by 121 publications

References 7 publications

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

Machine learning for fake news classification with optimal feature selection

A Novel Stacking Approach for Accurate Detection of Fake News

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