Location and audience selection for maximizing social influence

TL;DR

This paper introduces the Spreading Potential (SP) problem to evaluate diverse groups' influence in social networks, highlighting differences from influence maximization and proposing a new assessment method using statistical analysis.

Contribution

It defines the SP problem, distinguishes it from influence maximization, and develops a novel statistical method for evaluating influence measures on diverse agent sets.

Findings

Influence measures perform differently in SP compared to IM.

The new assessment method reveals significant differences in influence rankings.

Diverse target groups require different influence evaluation strategies.

Abstract

Viral marketing campaigns target primarily those individuals who are central in social networks and hence have social influence. Marketing events, however, may attract diverse audience. Despite the importance of event marketing, the influence of heterogeneous target groups is not well understood yet. In this paper, we define the Spreading Potential (SP) problem in which different sets of agents need to evaluated and compared based on their social influence. A typical application of SP is choosing locations for a series of marketing events. The SP problem is different from the well-known Influence Maximization (IM) problem in two aspects. Firstly, it deals with sets rather than nodes. Secondly, the sets are diverse, composed by a mixture of influential and ordinary agents. Thus, SP needs to assess the contribution of ordinary agents too, while IM only aims to find top spreaders. We provide a systemic test for ranking influence measures in the SP problem based on node sampling and on a novel statistical method, the Sum of Ranking Differences. Using a Linear Threshold diffusion model on an online social network, we evaluate seven network measures of social influence. We demonstrate that the statistical assessment of these influence measures is remarkably different in the SP problem, when low-ranked individuals are present, from the IM problem, when we focus on the algorithm's top choices exclusively.