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Title: Predicting political sentiments of voters from Twitter in multi-party contexts
Authors: Khatua, Aparup
Khatua, Apalak
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: 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-.
Journal: Applied Soft Computing Journal
Abstract: 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.
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2020.106743
Rights: © 2020 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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