Community-Based Prediction of Activity Decay in a Social Network
Name
Irene Teinemaa
Abstract
An important problem for facilitators of online social networks is to identify the users who are likely to decrease their level of activity in the near future. Such predictions are the basis for targeted campaigns aimed at sustaining or increasing the overall user engagement in the network. A common approach to this problem is to apply machine learning methods to make predictions at the level of individual users. The existing approaches, however, do not consider the social connections of the individuals to their full extent, leaving room for improvement.
In this context, we propose a new approach to the problem of activity decay prediction based on the idea of identifying groups of tightly inter-linked users (namely communities) where the level of social activity is likely to decay. We investigate two community detection methods and compare the resulting predictive accuracy against several baselines. We show that more individuals who are likely to decay can be reached by targeting communities instead of single users. Moreover, a bottom-up community detection method produces higher accuracy in this context than a top-down modularity-based approach. Additionally, a richer set of features related to user engagement can be used for prediction purposes, leading to more accurate predictions. The results pave the way for designing community-based approaches to analyze user engagement in social networks as well as associated community-based targeting methods.
Graduation Thesis language
English
Graduation Thesis type
Master - Software Engineering
Supervisor(s)
Anna Leontjeva
Defence year
2014