Abstract
Due to the constant evolvement of the web and the viral spread of online news on social media, predicting the popularity of a news article became a topic of interest to many categories of people ranging from marketing personnel to politicians. In this paper, we focus on comparing four classification algorithms on a dataset consisting of 39000 news articles taken from Mashable website. The articles were classified into two classes: Popular and not popular. Four different machine learning algorithms were used for classification of the data (KNN, Naïve bayes, Adaboost, and decision tree). Finally, the four classification methods were compared with each other.
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Alhalaseh, R., Rodan, A., Alazzam, A. (2020). A Classification Model for Modeling Online Articles. In: Brito-Loeza, C., Espinosa-Romero, A., Martin-Gonzalez, A., Safi, A. (eds) Intelligent Computing Systems. ISICS 2020. Communications in Computer and Information Science, vol 1187. Springer, Cham. https://doi.org/10.1007/978-3-030-43364-2_7
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