Preliminary study of spam profile detection for social media using Markov Clustering: Case study on Javanese people

In this paper, we report our primary findings in detecting spam profiles on Facebook social media. As we already know, spam profiles on social media especially Facebook is often used as a place to spread spam. Spammers generally use wall post feature on Facebook to spread spam. In addition spammers are also targeting page on Facebook which is a community of many people. In this community, spammers can spread spam as desired. Based on that background, this paper aims is to provide solutions in order to reduce the impact from spammers by using Markov Clustering algorithm to detect spam profile. We used B-Cubed Metrics and F-Measure method to test how the clustering process performs. We obtained 220 profiles in Facebook which contains both normal profile and spam profile of Indonesian Facebook users at Javanese island. B-cubed metrics show accuracy 70% and 74% after applying majority voting to merge cluster into two cluster. F-Measure show accuracy 87% and 88% after applying majority voting. This indicates that algorithm has performed well.