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Fake news detection using ensemble techniques

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Abstract

Spreading false information or distorted news on social media with the intention of harming a person, group, or governmental entity is known as fake news. Gathering information from an online platform is an effortless process due to its speed, user-friendliness, and continuous updates. Nevertheless, this data is susceptible to personal biases or preferences, posing potential drawbacks for individuals or organizations involved. Consequently, it becomes crucial to employ computational techniques for identifying the dissemination of misinformation. Therefore, this study explored different machine learning models to classify the veracity of information by utilizing a dataset consisting of fake and real news. The analysis encompassed approximately 40,000 items, with roughly 20,000 items from each dataset category. The study utilized a combination of ensemble learning models, such as support vector machine, logistic regression, catboost, Xgboost, multinomial, naive Bayes, and random forest. The performance of these models was assessed using diverse evaluation metrics, including recall, accuracy, false rejection rate, F1 score, precision, negative predictive value, false discovery rate, and Matthews' correlation coefficient. Following these analyses, the deep auto_ViML model and passive-aggressive classifier were calculated alongside the best learning models. After calculations, the deep Auto_ViML model was found to have the highest accuracy, precision, recall, and F1 score of 99%. On the other hand, the hybrid learning model achieved the most favorable false rejection rate at 71%. In terms of computational efficiency, the support vector machine proved to be the fastest, taking only 0.245 ms to compute.

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Data availability

The dataset used in this study is available on URL: https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset

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Authors and Affiliations

  1. University School of Information, Communication & Technology USIC&T, GGS Indraprastha University, Dwarka, New Delhi, India

    Pooja Malhotra & S. K. Malik

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  1. Pooja Malhotra
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Correspondence to Pooja Malhotra.

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Malhotra, P., Malik, S.K. Fake news detection using ensemble techniques. Multimed Tools Appl 83, 42037–42062 (2024). https://doi.org/10.1007/s11042-023-17301-w

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