research-article

Predicting political affiliation of posts on Facebook

Authors:

Che-Chia Chang,

Kuo-Wei HsuAuthors Info & Claims

IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

Article No.: 57, Pages 1 - 8

https://doi.org/10.1145/3022227.3022283

Published: 05 January 2017 Publication History

Abstract

Recently, social media such as Facebook has been more popular. Receiving information from Facebook and generating or spreading information on Facebook every day has become a general lifestyle. This new information-exchanging platform contains a lot of meaningful messages including users' emotions and preferences. Using messages on Facebook or in general social media to predict the election result and political affiliation has been a trend. In Taiwan, for example, almost every politician tries to have public opinion polls by using social media; almost every politician has his or her own fan page on Facebook, and so do the parties. We make an effort to predict to what party, DPP or KMT, two major parties in Taiwan, a post would be related or affiliated. We design features and models for the prediction, and we evaluate as well as compare them with the data collected from several political fan pages on Facebook. The results show that we can obtain accuracy higher than 90% when the text and interaction features are used with a nearest neighbor classifier.

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Cited By

Chiu S(2024)Deep Learning Models for Predicting Political Tendency in Facebook Posts Incorporating Texts and Emojis2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)10.1109/DOCS63458.2024.10704299(294-300)Online publication date: 16-Aug-2024
https://doi.org/10.1109/DOCS63458.2024.10704299
Morini VPollacci LRossetti G(2021)Toward a Standard Approach for Echo Chamber Detection: Reddit Case StudyApplied Sciences10.3390/app1112539011:12(5390)Online publication date: 10-Jun-2021
https://doi.org/10.3390/app11125390
Shetty NTeja DVinil TKanwal SMutha HBhargava A(2021)Inferring political preference from Twitter tweets2021 6th International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT50816.2021.9358593(471-475)Online publication date: 20-Jan-2021
https://doi.org/10.1109/ICICT50816.2021.9358593
Show More Cited By

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Predicting political affiliation of posts on Facebook

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cover image ACM Conferences

IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

January 2017

746 pages

ISBN:9781450348881

DOI:10.1145/3022227

Copyright © 2017 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: 05 January 2017

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IMCOM '17

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IMCOM '17: The 11th International Conference on Ubiquitous Information Management and Communication

January 5 - 7, 2017

Beppu, Japan

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IMCOM '17 Paper Acceptance Rate 113 of 366 submissions, 31%;

Overall Acceptance Rate 213 of 621 submissions, 34%

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Cited By

Chiu S(2024)Deep Learning Models for Predicting Political Tendency in Facebook Posts Incorporating Texts and Emojis2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)10.1109/DOCS63458.2024.10704299(294-300)Online publication date: 16-Aug-2024
https://doi.org/10.1109/DOCS63458.2024.10704299
Morini VPollacci LRossetti G(2021)Toward a Standard Approach for Echo Chamber Detection: Reddit Case StudyApplied Sciences10.3390/app1112539011:12(5390)Online publication date: 10-Jun-2021
https://doi.org/10.3390/app11125390
Shetty NTeja DVinil TKanwal SMutha HBhargava A(2021)Inferring political preference from Twitter tweets2021 6th International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT50816.2021.9358593(471-475)Online publication date: 20-Jan-2021
https://doi.org/10.1109/ICICT50816.2021.9358593
Rahaman IHosein P(2021)Extending DeGroot Opinion Formation for Signed Graphs and Minimizing PolarizationComplex Networks & Their Applications IX10.1007/978-3-030-65351-4_24(298-309)Online publication date: 5-Jan-2021
https://doi.org/10.1007/978-3-030-65351-4_24
Vicario MQuattrociocchi WScala AZollo F(2019)Polarization and Fake NewsACM Transactions on the Web10.1145/331680913:2(1-22)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3316809
Chiu SLin CLim WChiu MChen TJang J(2019)Predicting Neurodegenerative Diseases Using a Novel Blood Biomarkers-based Model by Machine Learning2019 International Conference on Technologies and Applications of Artiﬁcial Intelligence (TAAI)10.1109/TAAI48200.2019.8959854(1-6)Online publication date: Nov-2019
https://doi.org/10.1109/TAAI48200.2019.8959854
Chiu SHsu K(2018)Predicting Political Tendency of Posts on FacebookProceedings of the 2018 7th International Conference on Software and Computer Applications10.1145/3185089.3185094(110-114)Online publication date: 8-Feb-2018
https://dl.acm.org/doi/10.1145/3185089.3185094

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