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dc.contributor.authorMUTLU, Büşra
dc.contributor.authorMUTLU, Merve
dc.contributor.authorÖZTOPRAK, Kasım
dc.contributor.authorDOĞDU, Erdoğan
dc.date.accessioned2020-08-07T12:58:47Z
dc.date.available2020-08-07T12:58:47Z
dc.date.issued2016
dc.identifier10.1109/BigData.2016.7840796
dc.identifier.issn9781467390040 (ISBN)
dc.identifier.urihttp://hdl.handle.net/20.500.12498/3062
dc.description.abstractTrolls 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. © 2016 IEEE.
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.source4th IEEE International Conference on Big Data, Big Data 2016
dc.titleIdentifying trolls and determining terror awareness level in social networks using a scalable framework
dc.typeKonferans Bildirisi


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