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https://hdl.handle.net/20.500.11851/6850
Title: | Identifying Trolls and Determining Terror Awareness Level in Social Networks Using a Scalable Framework | Authors: | Mutlu, Büşra Mutlu, Merve Öztoprak, Kasım Doğdu, Erdoğan |
Keywords: | Troll detection kNN Naive Bayes C4.5 terrorism awareness |
Publisher: | IEEE | Source: | 4th IEEE International Conference on Big Data (Big Data) -- DEC 05-08, 2016 -- Washington, DC | Abstract: | Trolls in social media are 'malicious' users trying to propagate an opinion or distort the general perceptions. Identifying trolls in social media is a task of interest for many big data applications since data cannot be analyzed effectively without eliminating such users from the crowd. In this paper, we present a solution for troll detection and also the results of measuring terror awareness among social media users. We used Twitter platform only, and applied several machine learning techniques and big data methodologies. For machine learning we used k-Nearest Neighbour (kNN), Naive Bayes, and C4.5 decision tree algorithms. Hadoop/Mahout and Hadoop/Hive platforms were used for big data processing. Our tests show that C4.5 has a better performance on troll detection. | URI: | https://hdl.handle.net/20.500.11851/6850 | ISBN: | 978-1-4673-9005-7 |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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