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- W4376608091 abstract "Social media and its regular use is an important part of our life. It is regarded as one of the most crucial information sources in comparison with traditional ones. Among, Twitter has become one of the most popular social media platforms for communicating news, ideas, and emotions. The detection of the news creditability is an important analysis. In this paper, we proposed machine-based news social media creditability assessment and comparative analysis of predicted results using of the various algorithms. Finding new features which will use for predictive analysis and to improve performance of classifiers is one of the credibility detection challenges. Naive Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNNs), random forest (RF), and decision tree (DT) were applied over Fake News Net, LAIR, and SemEval datasets for the purpose of analysis assessment. SVM-based creditability assessment provides good results using Fake News Net dataset. SVM gives accuracy (72%) and (70%) for Naïve Bayes for the same dataset. Using both types of features and a stack classifier, we were able to attain the best results. Performance is measured by different measurements accuracy, precision, recall, and F1-score." @default.
- W4376608091 created "2023-05-17" @default.
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- W4376608091 date "2023-01-01" @default.
- W4376608091 modified "2023-10-14" @default.
- W4376608091 title "Comparative Analysis of Social Media Creditability Assessment Using Machine Learning Approaches" @default.
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- W4376608091 doi "https://doi.org/10.1007/978-981-19-9304-6_24" @default.
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