Predicting political sentiments of voters from Twitter in multi-party contexts
Prior Twitter-based electoral research has mostly ignored multi-party contexts and ‘mix tweets’ that jointly mention more than one party. Hence, we investigate the complex nature of these mix tweets in a multi-party context, and we argue mix tweeting patterns of users implicitly capture their politi...
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sg-ntu-dr.10356-1552672022-03-07T07:20:49Z Predicting political sentiments of voters from Twitter in multi-party contexts Khatua, Aparup Khatua, Apalak Cambria, Erik School of Computer Science and Engineering Engineering::Computer science and engineering Political Learning Multi-party Context Prior Twitter-based electoral research has mostly ignored multi-party contexts and ‘mix tweets’ that jointly mention more than one party. Hence, we investigate the complex nature of these mix tweets in a multi-party context, and we argue mix tweeting patterns of users implicitly capture their political opinions. We predict the political leaning of users based on their mix tweeting patterns in the context of the 2014 Indian General Election. We have agglomerated 2.4 million tweets from 0.15 million unique users. Next, we employ a multinomial logit regression model to test the hypothesized causal relation between mix tweeting patterns and the political leaning of users. Additionally, we also employ neural network-based algorithms to predict political leaning. Our study demonstrates that user-level mix-tweeting patterns can reveal the political opinions of Twitter users. 2022-03-07T07:20:49Z 2022-03-07T07:20:49Z 2020 Journal Article Khatua, A., Khatua, A. & Cambria, E. (2020). Predicting political sentiments of voters from Twitter in multi-party contexts. Applied Soft Computing Journal, 97(Part A), 106743-. https://dx.doi.org/10.1016/j.asoc.2020.106743 1568-4946 https://hdl.handle.net/10356/155267 10.1016/j.asoc.2020.106743 2-s2.0-85093692222 Part A 97 106743 en Applied Soft Computing Journal © 2020 Elsevier B.V. All rights reserved. |
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Engineering::Computer science and engineering Political Learning Multi-party Context Khatua, Aparup Khatua, Apalak Cambria, Erik Predicting political sentiments of voters from Twitter in multi-party contexts |
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Prior Twitter-based electoral research has mostly ignored multi-party contexts and ‘mix tweets’ that jointly mention more than one party. Hence, we investigate the complex nature of these mix tweets in a multi-party context, and we argue mix tweeting patterns of users implicitly capture their political opinions. We predict the political leaning of users based on their mix tweeting patterns in the context of the 2014 Indian General Election. We have agglomerated 2.4 million tweets from 0.15 million unique users. Next, we employ a multinomial logit regression model to test the hypothesized causal relation between mix tweeting patterns and the political leaning of users. Additionally, we also employ neural network-based algorithms to predict political leaning. Our study demonstrates that user-level mix-tweeting patterns can reveal the political opinions of Twitter users. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Khatua, Aparup Khatua, Apalak Cambria, Erik |
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Article |
author |
Khatua, Aparup Khatua, Apalak Cambria, Erik |
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Khatua, Aparup |
title |
Predicting political sentiments of voters from Twitter in multi-party contexts |
title_short |
Predicting political sentiments of voters from Twitter in multi-party contexts |
title_full |
Predicting political sentiments of voters from Twitter in multi-party contexts |
title_fullStr |
Predicting political sentiments of voters from Twitter in multi-party contexts |
title_full_unstemmed |
Predicting political sentiments of voters from Twitter in multi-party contexts |
title_sort |
predicting political sentiments of voters from twitter in multi-party contexts |
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2022 |
url |
https://hdl.handle.net/10356/155267 |
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1726885529862012928 |