Bara, Jacques, Lev, Omer and Turrini, Paolo (2022) Predicting voting outcomes in the presence of communities, echo chambers and multiple parties. Artificial Intelligence, 312 . 103773. doi:10.1016/j.artint.2022.103773 ISSN 0004-3702.

Preview

PDF WRAP-Predicting-voting-outcomes-presence-communities-echo-chambers-multiple-22.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1179Kb) | Preview

Request Changes to record.

Abstract

When individuals interact in a social network their opinions can change, at times quite significantly, as a result of social influence.

In elections, for example, while they might initially support one candidate, what their friends say may lead them to support another. But how do opinions settle in a social network, as a result of social influence?

A recently proposed graph-theoretic metric, the influence gap, has shown to be a reliable predictor of the effect of social influence in two-party elections, albeit only tested on regular and scale-free graphs. Here, we investigate whether the influence gap is able to predict the outcome of multi-party elections on networks exhibiting community structure, i.e., made of highly interconnected components, and therefore more resembling of real-world interaction. To encode communities we build on the classical model of caveman graphs, which we extend to a richer graph family that displays different levels of homophily, i.e., how much connections and opinions are intertwined.

Our contribution is three-fold. First, we study the predictive power of the influence gap in the presence of communities. We show that when there is no clear initial majority the influence gap is not a good predictor of the election outcome. When we instead allow for varying majorities, although the influence gap improves as a predictor, counting the initial partisan majority does consistently better, across all levels of homophily. Second, we study the combined effect of the more predictive metrics, as function of the homophily levels. Using regression models, we demonstrate that the influence gap combined with the initial votes count does increase the overall predictive power for some levels of homophily. Third, we study elections with more than two parties. Specifically, we extend the definition of the influence gap to any number of parties, considering various generalisations, and show that the initial votes count has an even higher predictive power when compared to influence gap than it did in the two-party case.

Item Type:	Journal Article
Subjects:	H Social Sciences > HM Sociology J Political Science > JC Political theory
Divisions:	Faculty of Science, Engineering and Medicine > Science > Mathematics
SWORD Depositor:	Library Publications Router
Library of Congress Subject Headings (LCSH):	Social networks , Social networks -- Mathematical models, Public opinion -- Mathematical models, Online social networks, Voting -- Mathematical models, Social influence
Journal or Publication Title:	Artificial Intelligence
Publisher:	Elsevier
ISSN:	0004-3702
Official Date:	12 August 2022
Dates:	Date Event 12 August 2022 Published 8 August 2022 Accepted
Volume:	312
Article Number:	103773
DOI:	10.1016/j.artint.2022.103773
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	15 August 2022
Date of first compliant Open Access:	15 August 2022
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 1965/20 Israel Science Foundation http://dx.doi.org/10.13039/501100003977 I-2527-407.6/2019 Global Innovation Fund https://www.globalinnovation.fund/ EP/S022244/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266
Related URLs:	http://creativecommons.org/licenses/by/4...

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads