1. A comparative analysis for detecting fake news using supervised learning algorithms.
- Author
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Dixit, Dheeraj Kumar, Bhagat, Amit, and Dangi, Dharmendra
- Subjects
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FAKE news , *COMPARATIVE studies , *RANDOM forest algorithms , *DECISION trees , *DATA scrubbing , *MACHINE learning - Abstract
Fake news is a type of essential problem on social media. The rapid circulate of fake news has an ability for disastrous influences on human beings and the society. Thus, it becomes more useful to detect fake news on social sites or internet. Recently, many models have been developed to detect the fake news from the publicly available datasets. In this paper discussed the various machine learning algorithms and their performance analysis on two different news data. The proposed framework contains two step process. In the first step, clean the data and extracted features by TF-IDF and Hashing Vectorizer. In the second step, machine learning algorithms (Logistic regression, Decision Tree, Random Forest, Multinomial Naive Bayes, and Passive Aggressive classifier) have been applied in an effective and efficient manner. Comparative analysis revealed that the optimal performance is achieved by the Logistic regression and Passive aggressive classifier, 95.45% and 97.35% respectively for two public datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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