Prediction of liking of functions based on the properties of features on social networking sites

Abstract

When developing a website, feedback from the community is imperative. This is not different for Social Networking Sites (SNS). There is no way however to measure how well the functionality is liked. During this research, a new method- ology is developed that will be able to predict the liking of functions based on features of the site. This will hopefully enable the creators of SNSs to better their sites to increase the number of visitors. For this, first the functions have been de- fined for the 3 current leading SNSs - Facebook, Google+ and LinkedIn. For this list of functions, lists of features were generated by users. With a question- naire, a dataset is generated, from which the relation between the features and functions can be derived by machine learning. In the end, three achievements are gained: better understanding of functions, features and the relationship between each other, that are common to these three SNSs, predictors, which takes ratings from a questionnaire as input, that predict how much a user likes a function, and a dataset containing ratings from 125 users.Web Information SystemsComputer ScienceElectrical Engineering, Mathematics and Computer Scienc