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Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

Computers, Materials & Continua 2023年第5期75卷 2921-2938页

作者： Muhammad Asfand-e-Yar qadeer hashir Syed Hassan Tanvir Wajeeha Khalil Department of Computer Science Center of Excellence in Artificial Intelligence(CoE-AI)Bahria UniversityIslamabad44000Pakistan Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawar25000Pakistan

The growth of the internet and technology has had a significant effect on social *** information has become an important research topic due to the massive amount of misinformed content on social *** is very easy for any user to spread misinformation through the ***,misinformation is a problem for professionals,organizers,and ***,it is essential to observe the credibility and validity of the News articles being shared on social *** core challenge is to distinguish the difference between accurate and false *** studies focus on News article content,such as News titles and descriptions,which has limited their ***,there are two ordinarily agreed-upon features of misinformation:first,the title and text of an article,and second,the user *** the case of the News context,we extracted different user engagements with articles,for example,tweets,i.e.,read-only,user retweets,likes,and *** calculate user credibility and combine it with article content with the user’s *** combining both features,we used three Natural language processing(NLP)feature extraction techniques,i.e.,Term Frequency-Inverse Document Frequency(TF-IDF),Count-Vectorizer(CV),and Hashing-Vectorizer(HV).Then,we applied different machine learning classifiers to classify misinformation as real or ***,we used a Support Vector Machine(SVM),Naive Byes(NB),Random Forest(RF),Decision Tree(DT),Gradient Boosting(GB),and K-Nearest Neighbors(KNN).The proposed method has been tested on a real-world dataset,i.e.,“fakenewsnet”.We refine the fakenewsnet dataset repository according to our required *** dataset contains 23000+articles with millions of user *** highest accuracy score is 93.4%.The proposed model achieves its highest accuracy using count vector features and a random forest *** discoveries confirmed that the proposed classifier would effectively classify misinformat

关键词： Misinformation user credibility fake news machine learning

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