Toward Early and Order-of-Magnitude Cascade Prediction in Social Networks

TL;DR

This paper introduces structural diversity measures to predict early viral cascades in social networks, achieving high accuracy in distinguishing viral from non-viral information spread despite data imbalance.

Contribution

It proposes novel structural diversity features for cascade prediction and demonstrates their effectiveness in early viral detection, outperforming existing methods.

Findings

Achieved 0.69 precision and 0.52 recall in early viral cascade prediction.

Structural diversity measures effectively distinguish viral from non-viral cascades.

Method performs well even with severe class imbalance.

Abstract

When a piece of information (microblog, photograph, video, link, etc.) starts to spread in a social network, an important question arises: will it spread to viral proportions - where viral can be defined as an order-of-magnitude increase. However, several previous studies have established that cascade size and frequency are related through a power-law - which leads to a severe imbalance in this classification problem. In this paper, we devise a suite of measurements based on structural diversity - the variety of social contexts (communities) in which individuals partaking in a given cascade engage. We demonstrate these measures are able to distinguish viral from non-viral cascades, despite the severe imbalance of the data for this problem. Further, we leverage these measurements as features in a classification approach, successfully predicting microblogs that grow from 50 to 500 reposts with precision of 0.69 and recall of 0.52 for the viral class - despite this class comprising under 2% of samples. This significantly outperforms our baseline approach as well as the current state-of-the-art. We also show this approach also performs well for identifying if cascades observed for 60 minutes will grow to 500 reposts as well as demonstrate how we can tradeoff between precision and recall.