Adam Tsakalidis
Predicting elections for multiple countries using Twitter and polls
Tsakalidis, Adam; Papadopoulos, S.; Cristea, A.I.; Kompatsiaris, Yiannis
Authors
S. Papadopoulos
Professor Alexandra Cristea alexandra.i.cristea@durham.ac.uk
Professor
Yiannis Kompatsiaris
Abstract
The authors' work focuses on predicting the 2014 European Union elections in three different countries using Twitter and polls. Past works in this domain relying strictly on Twitter data have been proven ineffective. Others, using polls as their ground truth, have raised questions regarding the contribution of Twitter data for this task. Here, the authors treat this task as a multivariate time-series forecast, extracting Twitter- and poll-based features and training different predictive algorithms. They've achieved better results than several past works and the commercial baseline.
Citation
Tsakalidis, A., Papadopoulos, S., Cristea, A., & Kompatsiaris, Y. (2015). Predicting elections for multiple countries using Twitter and polls. IEEE Intelligent Systems, 30(2), 10-17. https://doi.org/10.1109/mis.2015.17
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 5, 2015 |
Online Publication Date | Jan 26, 2015 |
Publication Date | Mar 1, 2015 |
Deposit Date | Jul 11, 2018 |
Publicly Available Date | Jul 31, 2018 |
Journal | IEEE Intelligent Systems |
Print ISSN | 1541-1672 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 30 |
Issue | 2 |
Pages | 10-17 |
DOI | https://doi.org/10.1109/mis.2015.17 |
Related Public URLs | http://wrap.warwick.ac.uk/75812/ |
Files
Accepted Journal Article
(644 Kb)
PDF
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry
(2022)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search