Words are important: A textual content based identity resolution scheme across multiple online social networks

Abstract

Identity resolution of a person using various online social networks can enable an interested party to have a better and holistic understanding of former’s behavior and personality. Major challenges in developing a reliable and scalable matching scheme for online identities include non-availability of required information or having contradictory information for the same user across these networks. In this study, we present a scheme for identity matching which utilizes important features extracted from contents generated by or shared with users across one’s online social networks. With the help of natural language processing and text mining techniques, we extract and process parts-of-speech, symbols, emoticons, numbers, and high frequency words in user’s posts, tweets, retweets, and URLs. On the basis of experiments with ground truth Twitter–Facebook real datasets, this method achieved 91.2 percent accuracy in matching user’s identity across the user’s profiles. The main contribution of this paper is that this proposes a novel method for identity matching, which utilizes only the publicly available content information of online social network users. This method can be used alone for identity matching, or can be used along with other identity resolution frameworks to enhance their accuracy.