Predicting Voting Outcomes for Multi-Alternative Elections in Social Networks

In multi-alternative elections, messages in support of or against alternatives spread across online social media. To make decisions, individuals integrate various sources of information, including information from their friends in online social networks. As information may continuously update online, opinions of individuals also become dynamic, making it challenging to predict voting outcomes. In this paper, we propose a novel voting metric named minimal influence gap, which is based on the information structure induced within a social network, to predict voting outcomes in multi-alternative elections. We evaluate this metric as a predictor of voting outcomes across three popular models of voting behaviours: the Maximising Expected Utility model, the Local Dominance model, and the K-Pragmatist model. We test our metric on synthetic networks with a scale-free feature or a community structure, and on a real-world social network. Experimental results demonstrate that the minimal influence gap strongly correlates with voting outcomes in the three models, particularly in the Local Dominance model when voters persist in supporting their favourite alternative.