Cost-Sensitive Classifier for Spam Detection on News Media Twitter Accounts

Social media are increasingly being used as sources in mainstream news coverage. However, since news is so rapidly updating it is very easy to fall into the trap of believing everything as truth. Spam content usually refers to the information that goes viral and skews users' views on subjects. To this end, this paper introduces a new approach for detecting spam tweets using Cost-Sensitive Classifier that includes Random Forest. Tweets were first annotated manually and then four different sets of features were extracted from them. Afterward, four machine learning algorithms were cross-validated to determine the best base classifier for spam detection. Finally, class imbalanced problem was dealt by resampling and incorporating arbitrary misclassification costs into the learning process. Results showed that the proposed approach helped mitigate overfitting and reduced classification error by achieving an overall accuracy of 89.14% in training and 76.82% in testing.