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In order to classify whether given data is rumor or not, follow the procedure as shown in Figure 1.3.

Initially, we consider a rumor dataset (messages) from social network. Next, to process the data, data processing is used. After processing, it is required to extract features like user features, Tweet features, and comment features from processed Twitter data as shown in Table 1.2. Later, use any classification algorithm to classify rumors based on these features. Classification models classify and produce results. In order to detect whether a given text is a rumor or not, the most common approach is to simply tokenize the text and apply classification algorithms. There are many classification algorithms that exist, but only few algorithms give better results. They are algorithms like Naïve Bayes, SVM, Neural network with TF, Neural network with Keras, decision tree, random forest, Long Short Term Memory, etc. In this section two major classification algorithms are discussed.

Figure 1.2 Classification of rumor and non-rumor.

Figure 1.3 Rumor classification process.