Measuring Friendship Closeness: A Perspective of Social Identity Theory

TL;DR

This paper introduces new friendship closeness measures based on social identity theory, considering group information to improve prediction accuracy in online gaming social networks.

Contribution

It develops SIT-based quantitative measures that incorporate local and global group information, outperforming existing methods in predicting friendship behaviors.

Findings

Outperforms state-of-the-art methods by up to 23.2% in behavior prediction.

Achieves up to 34.2% improvement in online target recommendation.

Validated on three online gaming datasets with extensive experiments.

Abstract

Measuring the closeness of friendships is an important problem that finds numerous applications in practice. For example, online gaming platforms often host friendship-enhancing events in which a user (called the source) only invites his/her friend (called the target) to play together. In this scenario, the measure of friendship closeness is the backbone for understanding source invitation and target adoption behaviors, and underpins the recommendation of promising targets for the sources. However, most existing measures for friendship closeness only consider the information between the source and target but ignore the information of groups where they are located, which renders inferior results. To address this issue, we present new measures for friendship closeness based on the social identity theory (SIT), which describes the inclination that a target endorses behaviors of users inside the same group. The core of SIT is the process that a target assesses groups of users as them or us. Unfortunately, this process is difficult to be captured due to perceptual factors. To this end, we seamlessly reify the factors of SIT into quantitative measures, which consider local and global information of a target's group. We conduct extensive experiments to evaluate the effectiveness of our proposal against 8 state-of-the-art methods on 3 online gaming datasets. In particular, we demonstrate that our solution can outperform the best competitor on the behavior prediction (resp. online target recommendation) by up to 23.2% (resp. 34.2%) in the corresponding evaluation metric.