An Approach towards Fake News Detection using Machine Learning Techniques
Keywords:
Confusion Matrix, ISOT Dataset, Machine Learning, Navie Bayes, Logistic RegressionAbstract
In the digital age, the spread of false information has become a widespread and difficult problem. The Naive Bayes & logistic regression algorithms are used in this paper to provide a novel methodology for the detection of bogus news stories. The aim of this study is to improve the efficacy of the identification of fake news in digital material, consequently fostering information credibility and integrity within the digital ecosystem. We start this investigation by gathering a wide dataset of news articles from both reputable and phoney sources. We preprocess the textual input using techniques like tokenization, stop-word removal, and stemming to aid in feature extraction. During the feature selection phase, the term frequency-inverse document frequency (TF-IDF) is used to estimate the word importance of each article. Next, the Naive Bayes algorithm is used to divide news stories into two groups: phoney and real. In order to determine the probability that an article will fall into a particular category, Naive Bayes uses a probabilistic technique under the assumption that the characteristics (words) are conditionally independent. Logistic Regression models the probability of a news article being fake or genuine based on a set of relevant textual features. The primary goal of logistic regression is to achieve high accuracy in classifying news articles as fake or genuine, with an emphasis on feature engineering and model evaluation. The efficacy of the corresponding methods is determined by utilizing the confusion matrix to evaluate the correctness of the model. The findings suggest that Logistic Regression is effective in detecting fake news and contributes to the trustworthiness of information sources in the digital age.
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