Abstract: Today is an information age, and people of all ages use smartphones and social media to convey information. This information is not always reliable; sometimes false information is spread over social media. The proliferation of fake news is a major problem all around the world. Fake news is infiltrating human minds via social media. After reading the news articles on Facebook, WhatsApp, Instagram, Twitter, and other social media platforms, Sometimes these publications convey the inaccurate or incorrect message, which is referred to as fake news. This fake news is being a very big scam in which many people are being trapped. To solve this big issue, it’s important to develop a system called “Fake news detection system”. This fake news detection system detects the fake news by the prefilled data by various articles, from various sources. The earliest available systems uses some common methodology like Data Collection, Feature Extraction, Model Building and Model Evalution but only variations are seen in the use of techniques to solve the problem. This paper presents a Passive Aggressive Classifier Model with TfidfVectorizer Text Classification to address fake news. As per result obtained by proposed model it gives better accuracy than previous model used by researchers.

Keywords: Fake News Detection, Data Collection, Feature Extraction, Passive Aggressive Classifier, TfidfVectorizer

PDF | DOI: 10.17148/IJARCCE.2023.124134

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