Abstract
Social media and the Internet have suffered from false messages and false news since the outbreak of the COVID-19 pandemic. The intention is often to mislead readers and/or make them believe something that is not real. All that increases the need for automatic methods that can detect fake news in social media. In this paper, we proposed a classification model based on machine learning and deep learning algorithms to classify COVID-19 tweets into two classes using Apache Spark and the Python API Tweepy, the proposed idea uses the features of tweets to detect fake news. Experimental results show that the random forest algorithm gives best results with an accuracy equal to 79% and that the sentiment of tweets plays an important role in the detection of fake news. By applying the proposed model on our COVID-19 dataset, 67% of tweets are classified as REAL while 37% are classified FAKE.
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Notes
- 1.
TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
- 2.
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Madani, Y., Erritali, M., Bouikhalene, B. (2021). Fake News Detection Approach Using Parallel Predictive Models and Spark to Avoid Misinformation Related to Covid-19 Epidemic. In: Gherabi, N., Kacprzyk, J. (eds) Intelligent Systems in Big Data, Semantic Web and Machine Learning. Advances in Intelligent Systems and Computing, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-72588-4_13
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