Identity matching in social media platforms

Identity matching is the process of mapping profile information from disparate data sources to one single entity; this is a crucial task for many businesses and governments. Introduction of Web 2.0 and the ever increasing number of social media platforms has led to an explosive amount of user participation and collaboration on web. An ordinary user has more than one social media profile, each of which has a unique set of properties and features. This thesis proposes a framework that uses syntactic and semantic based identity matching approaches among Facebook, Linkedin and Twitter user profiles. The framework accomplishes this task by collecting available profile data and performing analysis and comparison using a set of methodologies. These methods consist of weighted string matching techniques, Google Maps, YouTube and NLP web APIs. Extracted Profiles with a similarity score above a pre-computed threshold value are considered a match.