research-article

Show me your friends, and I will tell you whom you vote for: Predicting voting behavior in social networks

Authors:

Joan FeigenbaumAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 816 - 824

https://doi.org/10.1145/3341161.3343676

Published: 15 January 2020 Publication History

Abstract

Increasing use of social media in campaigns raises the question of whether one can predict the voting behavior of social-network users who do not disclose their political preferences in their online profiles. Prior work on this task only considered users who generate politically oriented content or voluntarily disclose their political preferences online. We avoid this bias by using a novel Bayesian-network model that combines demographic, behavioral, and social features; we apply this novel approach to the 2016 U.S. Presidential election. Our model is highly extensible and facilitates the use of incomplete datasets. Furthermore, our work is the first to apply a semi-supervised approach for this task: Using the EM algorithm, we combine labeled survey data with unlabeled Facebook data, thus obtaining larger datasets as well as addressing self-selection bias.

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Paul JDas Chatterjee AMisra DMajumder SRana SGain MDe AMallick SSil J(2024)A survey and comparative study on negative sentiment analysis in social media dataMultimedia Tools and Applications10.1007/s11042-024-18452-083:30(75243-75292)Online publication date: 15-Feb-2024
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Show me your friends, and I will tell you whom you vote for: Predicting voting behavior in social networks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 January 2020

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US Office of Naval Research
US National Science Foundation
William and Flora Hewlett Foundation

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ASONAM '19

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SIGKDD

ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

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https://doi.org/10.1111/1475-679X.12518
Alexi ALazebnik TRosenfeld A(2024)The scientometrics and reciprocality underlying co-authorship panels in Google Scholar profilesScientometrics10.1007/s11192-024-05026-y129:6(3303-3313)Online publication date: 27-May-2024
https://doi.org/10.1007/s11192-024-05026-y
Paul JDas Chatterjee AMisra DMajumder SRana SGain MDe AMallick SSil J(2024)A survey and comparative study on negative sentiment analysis in social media dataMultimedia Tools and Applications10.1007/s11042-024-18452-083:30(75243-75292)Online publication date: 15-Feb-2024
https://doi.org/10.1007/s11042-024-18452-0
Kumar SUddin Haider M(2023)Sentiment Analysis of Political Party Twitter Data using Ensemble Learning Classifier2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10308149(1-5)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICCCNT56998.2023.10308149
Chauhan PSharma NSikka G(2023)Application of Twitter sentiment analysis in election prediction: a case study of 2019 Indian general electionSocial Network Analysis and Mining10.1007/s13278-023-01087-813:1Online publication date: 18-May-2023
https://doi.org/10.1007/s13278-023-01087-8
Idan L(2022)Beyond Purchase Intentions: Mining Behavioral Intentions of Social-Network UsersInternational Journal of Human–Computer Interaction10.1080/10447318.2022.213219540:5(1111-1132)Online publication date: 26-Oct-2022
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Mainz JSønderholm JUhrenfeldt R(2022)Artificial intelligence and the secret ballotAI & SOCIETY10.1007/s00146-022-01551-739:2(515-522)Online publication date: 1-Sep-2022
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Levine K(2021)It’s Okay to Change Your Mind: You Do Not Need to Vote the Same Way TwiceAmerican Behavioral Scientist10.1177/0002764221100927766:3(376-389)Online publication date: 29-Apr-2021
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Santos JBernardini FPaes A(2021)A survey on the use of data and opinion mining in social media to political electoral outcomes predictionSocial Network Analysis and Mining10.1007/s13278-021-00813-411:1Online publication date: 20-Oct-2021
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