Shaping Social Activity by Incentivizing Users

TL;DR

This paper models social network events with Hawkes processes to determine optimal external incentives for steering overall activity, demonstrated through Twitter data experiments.

Contribution

It introduces a convex optimization framework linking exogenous event intensity to network activity, enabling targeted influence in social networks.

Findings

The method accurately steers network activity towards desired levels.

It outperforms alternative approaches in experiments with Twitter data.

Provides a quantitative model for external influence on social activity.

Abstract

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.