Please use this identifier to cite or link to this item: 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
Issue Date: 2016
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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