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This paper presents the results and conclusions of our participation in SemEval-2017 task 8: Determining rumour veracity and support for rumours. We have participated in 2 subtasks: SDQC (Subtask A) which deals with tracking how tweets orient to the accuracy of a rumourous story, and Veracity Prediction (Subtask B) which deals with the goal of predicting the veracity of a given rumour. Our participation was in the closed task variant, in which the prediction is made solely from the tweet itself. For subtask A, linear support vector classification was applied to a model of bag of words, and the help of a naïve Bayes classifier was used for semantic feature extraction. For subtask B, a similar approach was used. Many features were used during the experimentation process but only a few proved to be useful with the data set provided. Our system achieved 71% accuracy and ranked 5th among 8 systems for subtask A and achieved 53% accuracy with the lowest RMSE value of 0.672 ranking at the first place among 5 systems for subtask B.

NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter.