Signed Link Prediction with Sparse Data: The Role of Personality Information

TL;DR

This paper explores how incorporating user personality traits, such as optimism and pessimism, can improve signed link prediction in social networks, especially under conditions of data sparsity.

Contribution

It introduces a novel model that leverages social media data to obtain personality information, addressing the challenge of signed link data sparsity.

Findings

Personality information enhances signed link prediction accuracy.

Different levels of personality data contribute variably to alleviating data sparsity.

The model performs well on real-world signed social network datasets.

Abstract

Predicting signed links in social networks often faces the problem of signed link data sparsity, i.e., only a small percentage of signed links are given. The problem is exacerbated when the number of negative links is much smaller than that of positive links. Boosting signed link prediction necessitates additional information to compensate for data sparsity. According to psychology theories, one rich source of such information is user's personality such as optimism and pessimism that can help determine her propensity in establishing positive and negative links. In this study, we investigate how personality information can be obtained, and if personality information can help alleviate the data sparsity problem for signed link prediction. We propose a novel signed link prediction model that enables empirical exploration of user personality via social media data. We evaluate our proposed model on two datasets of real-world signed link networks. The results demonstrate the complementary role of personality information in the signed link prediction problem. Experimental results also indicate the effectiveness of different levels of personality information for signed link data sparsity problem.