Authors : C. Emilin Shyni, Anesh D. Sundar and G.S. Edwin Ebby
Abstract: Online Social networks, popularly known as OSN are widely used by millions of people around the world to communicate with friends and relatives. Information sharing is done by sending links to videos, websites and files. The community structure of the online social networks help in building a network of trust which is exploited by spammers who spread spam messages that promote personal blogs, advertisements, phishing and scam. Spamming is the method of sending unsolicited bulk messages especially advertisements, indiscriminately. Two of the most popularly used OSN around the world are Facebook and Twitter. This research focuses on detecting spam profiles on the given set of Twitter profiles. Current studies identify spam profiles based on a set of 11 features. This study identifies spam profiles based on an enriched set of 17 features. The extracted features are given as input to classification algorithms and the accuracy of these different algorithms are analysed. Based on this, the best classification algorithm for Twitter has been identified.
C. Emilin Shyni, Anesh D. Sundar and G.S. Edwin Ebby, 2016. Spam Profile Detection in Online Social Network Using Statistical Approach. Asian Journal of Information Technology, 15: 1253-1262.