Diffusion of Ordinal Opinions in Social Networks: An Agent-Based Model and Heuristics for Campaigning

Abstract

Most research investigating how social influence affects election results mainly uses diffusion models for binary opinions. However, these diffusion models are progressive and focus on the diffusion of one opinion. In this article, we introduce a general diffusion model for ordinal opinions expressed as linear orderings over a finite set of candidates. We employ agent-based modeling to simulate a nonprogressive diffusion process, allowing multiple types of opinion diffusion about different candidates. The proposed agent-based diffusion model can forecast long-term trends of opinion diffusion in social networks by capturing voters’ personalized features and incorporating dynamic social contexts. Furthermore, we examine the possibility of affecting election outcomes by externally changing the ordinal opinions of certain vertices, i.e., campaigning. Since finding influential voters from the social network is computationally challenging, we propose a heuristic approach, i.e., backward influence rank (BIR). Experimental results demonstrate that the proposed BIR approach is superior to the classic greedy approach for campaigning by achieving a similar margin of victory to that of the greedy approach but running two orders of magnitude faster than the greedy approach did.