Abstract
In this paper, we propose a novel technique for classifying user accounts on online social networks. The main purpose of our classification is to distinguish the patterns of users from those of organizations and individuals. The ability of distinguishing between the two account types is needed for developing recommendation engines, consumer products opinion mining tools, and information dissemination platforms. However, such a task is non-trivial. Classic and consolidated approaches of text mining use textual features from natural language processing for classification. Nevertheless, such approaches still have some drawbacks like the computational cost and time consumption. In this work, we propose a statistical approach based on post frequency, metadata of user profile, and popularity of posts so as to recognize the type of users without textual content. We performed a set of experiments over a twitter dataset and learn-based algorithms in classification task. Several supervised learning algorithms were tested. We achieved high f-measure results of 96.2% using imbalanced datasets and (GBRT), 1.9% were gains when we used imbalanced datasets with Synthetic Minority Oversampling technique and (RF), this yields 98.1%.
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Daouadi, K.E., Zghal Rebaï, R., Amous, I. (2019). Towards a Statistical Approach for User Classification in Twitter. In: Renault, É., Mühlethaler, P., Boumerdassi, S. (eds) Machine Learning for Networking. MLN 2018. Lecture Notes in Computer Science(), vol 11407. Springer, Cham. https://doi.org/10.1007/978-3-030-19945-6_3
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DOI: https://doi.org/10.1007/978-3-030-19945-6_3
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