A comparative study for tweet popularity detection using machine learning

Internet media is very popular nowadays. Although printed media has not completely lost its popularity, it is possible for internet users to get news from alternative ways and to follow news through many different channels, which increases the popularity of internet media. An important issue of internet media is social media sharing. In this way, journalists can access most readers. Readers, on the other hand, can access the right news with the help of confirmed media users via social media. During all these changes, it is very important to ensure that news or articles are accessible to users and the interest they receive by internet media managers and writers. In this thesis, a comparison of the popularity of the shares made by the confirmed news organs on Twitter was made with the help of various machine learning techniques. Experiments were carried out using Support Vector Machines, Logistic Regression, Naive Bayes classifier, K Nearest Neighbor Algorithm, Random Forest and Deep Reinforcement Learning (Double Deep Q-Learning) methods. The best results from the methods used in the thesis study were obtained with K Nearest Neighbor, Random Forest and Deep Reinforcement Learning algorithms. The F1-score obtained with this method is 0.87. The worst F1-score result was obtained with the Naive Bayes classifier. Naive Bayes classifier gave 0.63 results. An F1-score of 0.84 for linear SVC, 0.88 for radial basis kernel function SVC and 0.83 for logistic regression was obtained.