Influence and Passivity in Social Media

TL;DR

This paper investigates how influence in social media is affected by user passivity, proposing an algorithm to measure influence considering forwarding activity, and demonstrating that popularity does not always equate to influence.

Contribution

It introduces a novel influence measure that accounts for user passivity and validates its effectiveness using a large Twitter dataset.

Findings

Influence measure predicts URL clicks better than existing metrics.

Most users are passive consumers and rarely forward content.

High popularity does not necessarily mean high influence.

Abstract

The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We also explicitly demonstrate that high popularity does not necessarily imply high influence and vice-versa.