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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Main Authors: Khatua, Aparup, Khatua, Apalak, Cambria, Erik
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/155267
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Institution: Nanyang Technological University
Language: English
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spelling 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.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Political Learning
Multi-party Context
spellingShingle 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
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Khatua, Aparup
Khatua, Apalak
Cambria, Erik
format Article
author Khatua, Aparup
Khatua, Apalak
Cambria, Erik
author_sort 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
publishDate 2022
url https://hdl.handle.net/10356/155267
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