Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/12363
Title: Forecasting elections from VAA data: what the undecided would vote?
Authors: Tsapatsoulis, Nicolas 
Agathokleous, Marilena 
Keywords: Forecasting elections;Large samples;Undecided voters;VAA data;Vote prediction
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: Jul-2017
Source: 12th International Workshop on Semantic and Social Media Adaptation and Personalization, 2017, Bratislava, Slovakia, 9-10 July
DOI: https://doi.org/10.1109/SMAP.2017.8022666
Abstract: In many Voting Advice Applications (VAAs) a supplementary question concerning the voting intention of a VAA user is included. The data that are collected through this question can serve a variety of purposes, election forecast being one of them. However, it appears that the majority of VAA users who answer this question select safe choices such as 'I prefer not to say' and 'I am undecided'. In this study we investigate at what degree we can predict, with the aid of machine learning techniques, the voting intention of the above-mentioned users using as input their choices in the VAA policy statements. The results show an accuracy higher than 60%, supposed that sufficient training examples for each party that participates in the elections exist so as to model each party users. Also, it appears that there is significant difference on the distribution per party for the users who select 'I prefer not to say' and those who select 'I am undecided'. As a consequence of these findings one would suggest that for effective election forecast it is required to (a) distribute the VAA users who select the previously mentioned choices in the voting intention question in a more sophisticated and intelligent way than that followed in traditional poll methods, and (b) the VAA users who select each one of those choices should be handled separately.
URI: http://ktisis.cut.ac.cy/handle/10488/12363
Rights: © 2017 IEEE.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Page view(s) 1

39
Last Week
0
Last month
3
checked on Jul 21, 2019

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.