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 frequency-inverse 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 Novel Stacking Approach for Accurate Detection of Fake News

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

Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.

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A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

The widespread use of social media has had a terrible impact on our society due to the spread of fake news. In particular, the author has seen the idea of the quality of fake news before its origin. Like the internet, publishers used false and misleading information to further their interest. People often get involved in social media as social media provides low cost, quick access, and fast spread of news. It has been seen for many years that fake news harms persons as well as society. So, the challenge of fake news detection arrived. Inappropriate news took place to attract people so the sender can start putting the rumor of news. This had led to a negative impact on people about social media for their news. This led to inconvenience for offline news as well because when people will too much depend on online platforms that will reduce the offline users. This survey deals with a review of existing machine learning algorithms Naive Bayes, Convolutional Neural Network, LSTM, Neural Network, Support Vector Machine proposed for detecting and reducing fake news from different social media platforms like Facebook, whatsapp, twitter, etc. This review provides a comprehensive detail including data mining perspective, evaluation metrics, and representative datasheets. Further, a comparison of the state-of-the-art is presented and untackled challenges in detecting fake news are highlighted. Research in the field of detecting fake news has been hampered to a great extent by the lack of quantity and quality of existing datasets. Therefore, this review compares the existing approaches to build models and with further improvements to be expected by using the combination of different machine learning techniques.

A Review of Fake News Detection Methods using Machine Learning

Nowadays, current events related to diverse fields are published in newspapers, shared on social media and broadcasted on radio and television. The explosive growth in online news contents has made it very difficult to discriminate between real and fake. As a result, fake news has become prevalent and immensely challenging to analyze and verify. Indeed, it is a big challenge to the government and public to debate the situation depending on case to case. For this purpose, 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 classify fake news. However, these approaches are restricted in terms of accuracy. This study has applied a random forest (RF) classifier to predict fake or real news. For this purpose, 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 to select fourteen best features out of twenty-three. The proposed model and other benchmark techniques are evaluated on benchmark dataset using best features. Experimental findings 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 classification accuracy. 

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Machine learning for fake news classification with optimal feature selection

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication. The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites. It is harmful for the society to believe on the rumors and pretend to be a news. The need of an hour is to stop the rumors especially in the developing countries like India, and focus on the correct, authenticated news articles. This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and natural language processing, author tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine. The results of the proposed model is compared with existing models. The proposed model is working well and defining the correctness of results upto 93.6% of accuracy.

Avinash Shakya

Papers

A smart System for Fake News Detection Using Machine Learning