학술논문
Anomalous behavior detection in social networking
Document Type
Conference
Author
Source
2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT) Computing, Communication and Networking Technologies (ICCCNT), 2017 8th International Conference on. :1-5 Jul, 2017
Subject
Language
Abstract
Over the years, use of Online Social Networks (OSNs) has exploded and thus, causing a need of studying and understanding users' behavior online. The excessive use of online social networking causes a great increase in anomalies. Anomalies in OSNs can signify irregular and often illegal behavior. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators and online fraudsters. For detecting the anomalies dataset of Twitter network is used and analyzed for user behavior via analyzing their tweets to find whether it is an anomalous or not.