Abstract
Social media news reading is growing tremendously common these days. Audiences gain from social media because of its inherent features of rapid transmission, low cost, and ease of access. However, the news quality is seen as lower than that of established news channels, leading to a significant volume of fake news. Detecting false news has become increasingly important and is attracting increasing interest due to its negative effects on people and society. This paper proposes a system for classifying unreliable news by comparing the accuracy scores of various online Machine learning algorithms (Passive Aggressive Classifier, Multinomial Naive Bayes) using various types of feature extraction (count vectorizer, hashing vectorizer, term frequency inverse document vectorizer). The findings indicate that the problem of detecting false news may be effectively handled using online machine learning classifiers. When compared to others with the data set in consideration, the Passive Aggressive Classifier with TFIDF vectorizer gives the best accuracy of 93.7%.
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Manohar, P.M., Appala Naidu, P., Chandra Sekhar, A.V.N., Venkata Reddy, B.S. (2023). Fake News Detection and Analysis Using Online Machine Learning Techniques. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2746-3_6
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DOI: https://doi.org/10.1007/978-981-99-2746-3_6
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