Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155267
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dc.contributor.authorKhatua, Aparupen_US
dc.contributor.authorKhatua, Apalaken_US
dc.contributor.authorCambria, Eriken_US
dc.date.accessioned2022-03-07T07:20:49Z-
dc.date.available2022-03-07T07:20:49Z-
dc.date.issued2020-
dc.identifier.citationKhatua, 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.2020.106743en_US
dc.identifier.issn1568-4946en_US
dc.identifier.urihttps://hdl.handle.net/10356/155267-
dc.description.abstractPrior 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.en_US
dc.language.isoenen_US
dc.relation.ispartofApplied Soft Computing Journalen_US
dc.rights© 2020 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titlePredicting political sentiments of voters from Twitter in multi-party contextsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.asoc.2020.106743-
dc.identifier.scopus2-s2.0-85093692222-
dc.identifier.issuePart Aen_US
dc.identifier.volume97en_US
dc.identifier.spage106743en_US
dc.subject.keywordsPolitical Learningen_US
dc.subject.keywordsMulti-party Contexten_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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