Negative Link Prediction in Social Media

TL;DR

This paper introduces NeLP, a framework for predicting negative links in social media using only positive links and content interactions, addressing the lack of explicit negative link data.

Contribution

It proposes a novel negative link prediction method that leverages positive links and content data, filling a gap in signed network analysis.

Findings

NeLP accurately predicts negative links in real-world social networks.

Content-centric interactions significantly improve prediction accuracy.

Various factors influence the effectiveness of the proposed framework.

Abstract

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework.