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Scientific Transactions in Environment and Technovation


Scientific Transactions in Environment and Technovation, | 10.56343/STET.
Year : 2023 | Volume: 17 | Issue: 1 | Pages : 18-26


Abstract :

In the new media age, fake news has become a widely known occurrence. The spread of false news refers to the rapid dissemination of false or misleading information through various media platforms, often with the intention to device or manipulate the public, and can direct to debates, social media wars and hatred arguments. To detect fake news, our proposed framework extracts the data from the news articles and the social contexts. This proposed model is based on Machine Learning techniques, which has four components namely data storage, abstraction, generalization and evaluation. Challenge in fake news detection is to detect it in the earlier phase and the unavailability or the shortage of labelled data for training the detection models. In this paper, the dataset is chosen relatively to real and fake news detection. Determining the accuracy and precision of the entire dataset sets the objective of this paper. The analysis had been done using Python and the outcomes are envisioned in the form of graphs. The outcomes showed the certitude that the dataset grabs 95% of the accuracy. The number of actual predicted cases is coded and the result obtained is 296. Upshots of this paper reveals that the accuracy of the model dataset is 95.26 % the precision results 95.79 % whereas recall and F-Measure shows 94.56% and 95.17% accuracy respectively. There are 296 positive attributes , 308 negative attributes 17 false positives and 13 false negatives in the predicted models. This research advocates that legitimacy of news should be inspected first instead of framing an opinion and its performance is based on predicting real and fake news which speaks about the ethics of both journalists and news consumers.


Fake News Detection, Social Contexts, Machine Learning techniques, Python, Accuracy, Precision, F-Measure, Recall

Citation: *,

( 2023), FAKE AND REAL NEWS DETECTION USING MACHINE LEARNING TECHNIQUES AND PYTHON. Scientific Transactions in Environment and Technovation, 17(1): 18-26

Mr.Veerapathiran K

Correspondence: S.Seema

stet journals

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