TY - GEN
T1 - Predicting Voting Outcomes in Presence of Communities
AU - Bara, Jacques
AU - Lev, Omer
AU - Turrini, Paolo
N1 - Funding Information:
This research was partly supported by ISF grant 1965/20, GIF grant I-2527-407.6/2019 and EPSRC grant EP/S022244/1 for the MathSys CDT.
Publisher Copyright:
© 2021 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
PY - 2021
Y1 - 2021
N2 - Individuals in a social network may form their views as a result of the influence exerted by their connections. In elections, for example, while they might initially support one candidate, social influence may lead them to support another. Here, we investigate whether a recently proposed metric, influence gap, designed to measure the effect of social influence in voting on social networks, is able to predict the outcome of a vote on networks exhibiting community structure, i.e., made of highly interconnected components, and therefore more resembling of real-world interaction. To encode communities, we extend the classical model of caveman graphs to a richer graph family that displays levels of homophily, i.e., where connections and opinions are highly intertwined. We show that, across these graphs, there are important cases when the influence gap correlation is a weak predictor due to communities, and a simpler metric, counting the initial partisan majority, provides a more accurate prediction overall. Using regression models, we further demonstrate that the influence gap combined with the more successful metrics does increase their predictive power for some levels of homophily.
AB - Individuals in a social network may form their views as a result of the influence exerted by their connections. In elections, for example, while they might initially support one candidate, social influence may lead them to support another. Here, we investigate whether a recently proposed metric, influence gap, designed to measure the effect of social influence in voting on social networks, is able to predict the outcome of a vote on networks exhibiting community structure, i.e., made of highly interconnected components, and therefore more resembling of real-world interaction. To encode communities, we extend the classical model of caveman graphs to a richer graph family that displays levels of homophily, i.e., where connections and opinions are highly intertwined. We show that, across these graphs, there are important cases when the influence gap correlation is a weak predictor due to communities, and a simpler metric, counting the initial partisan majority, provides a more accurate prediction overall. Using regression models, we further demonstrate that the influence gap combined with the more successful metrics does increase their predictive power for some levels of homophily.
KW - Communities
KW - Opinion dynamics
KW - Social networks
KW - Voting
UR - http://www.scopus.com/inward/record.url?scp=85112229969&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 151
EP - 159
BT - 20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
Y2 - 3 May 2021 through 7 May 2021
ER -