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. © 2016 IEEE.
Eser Adı (dc.title) | Identifying trolls and determining terror awareness level in social networks using a scalable framework |
Yayın Türü (dc.type) | Konferans Bildirisi |
Yazar/lar (dc.contributor.author) | MUTLU, Büşra |
Yazar/lar (dc.contributor.author) | MUTLU, Merve |
Yazar/lar (dc.contributor.author) | ÖZTOPRAK, Kasım |
Yazar/lar (dc.contributor.author) | DOĞDU, Erdoğan |
DOI Numarası (dc.identifier.doi) | 10.1109/BigData.2016.7840796 |
Atıf Dizini (dc.source.database) | Scopus |
Yayıncı (dc.publisher) | Institute of Electrical and Electronics Engineers Inc. |
Yayın Tarihi (dc.date.issued) | 2016 |
Kayıt Giriş Tarihi (dc.date.accessioned) | 2020-08-07T12:58:47Z |
Açık Erişim tarihi (dc.date.available) | 2020-08-07T12:58:47Z |
Kaynak (dc.source) | 4th IEEE International Conference on Big Data, Big Data 2016 |
ISSN (dc.identifier.issn) | 9781467390040 (ISBN) |
Özet (dc.description.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. © 2016 IEEE. |
Yayın Dili (dc.language.iso) | en |
Tek Biçim Adres (dc.identifier.uri) | http://hdl.handle.net/20.500.12498/3062 |