Name
Title | ||
|---|---|---|
Identifying Trolls And Determining Terror Awareness Level In Social Networks Using A Scalable Framework |
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. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | Troll detection, kNN, Naive Bayes, C4.5, terrorism awareness |
Field | DocType | Citations |
Data science,Data mining,Decision tree,Social network,Computer science,Artificial intelligence,Big data processing,World Wide Web,Social media,Naive Bayes classifier,Statistical classification,Big data,Machine learning,Scalability | Conference | 1 |
PageRank | References | Authors |
0.35 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Busra Mutlu | 1 | 1 | 0.35 |
| Merve Mutlu | 2 | 1 | 0.35 |
| Kasim Oztoprak | 3 | 11 | 4.20 |
| Erdogan Dogdu | 4 | 195 | 41.17 |