A feasibility study for modelling tie strength with the facebook API

This thesis examines the concept of tie strength and investigates how it can be determined on the fly in the Facebook Social Network Service (SNS) by a system constructed using the standard developer API. We analyze and compare two different models: the first one is an adaptation of previous literature (Gilbert & Karahalios, 2009), the second model is built from scratch and based on a dataset obtained from an online survey. This survey took the form of a Facebook application that collected subjective ratings of the strength of 1642 ties (friendships) from 85 different participants. The new tie strength model was built based on this dataset by using a multiple regression method. We saw that the new model performed slightly better than the original adapted model, plus it had the advantage of being easier to implement. In conclusion, this thesis has shown that tie strength models capable of serving as useful friendship predictors are easily implementable in a Facebook application via standard API calls. In addition to a new tie strength model, the methodology adopted in this work permitted observation of the weights of each predictive variable used in the model, increasing the visibility of the factors that affects peoples’ relationships in online social networks.