Using Strong, Acquaintance and Weak Tie Strengths for Modeling Relationships in Facebook Network

Predicting strength of a relationship (also known as Tie Strength Problem) has been a trivial research area amongst sociologists for decades. However, considering the recent trends in internet behavior of people along with the development of so called social web, makes it popular amongst web scientists to work on this as a potential research topic with new perspectives. Real life is a complex social dynamic system comprising individuals starting of either as strong acquaintances or weak acquaintances and move towards strong or weak ties with passage of time. In this paper we validate the existence of varying degree of relationship individuals have on Facebook using unsupervised machine learning techniques like divisive hierarchical clustering and statistical techniques like SSE ; analyzing strength of the boundaries that distinguish them. We have realized this on a feature rich dataset of more than 100 nodes collected during 10th of July, 2011 to the 9th of September 2011 using a Facebook application. We provide descriptive error analysis interviews focussing on the clustered structure, obtaining it with an accuracy of 90%. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing and information prioritization in databases. Potential usage of this work can be in making complex recommender systems, lead generation marketing and in organizational or telecom network.