Detection of Fake News and Probability
DOI:
https://doi.org/10.61841/fg559k88Keywords:
Fake news, classifier, machine learning, data setsAbstract
The quality of information is an important issue in the present age. The preciseness of news or information through social media is very important, and it is an increasing problem in our society. Fake news is the misleading or wrong information that is spread over the internet to damage the popularity of a person or organization. To overcome the problem, research should be done to classify whether the news 'is'real’ or ‘fake’. To classify the news, a machine learning algorithm is adopted. In the process of classifying news, classifier algorithms like the Support Vector Machine algorithm, the Naive Bayes algorithm, the Decision Tree algorithm, the Random Forest algorithm, and the Logistic Regression classifier algorithm are required. Initially, data sets are extracted, and they should be processed by the machine learning algorithms. Buzzfeed, Credbank, and Phema are some of the datasets that can extract the required information from social media. In our model, the contents are processed through all the algorithms that will predict whether the news on social media is ‘fake’ 'or'real’ and the probability of the truth.
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