Predicting political sentiments of voters from Twitter in multi-party contexts
| Autor: | Aparup Khatua, Erik Cambria, Apalak Khatua |
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| Přispěvatelé: | School of Computer Science and Engineering |
| Rok vydání: | 2020 |
| Předmět: |
Multi-party Context
0209 industrial biotechnology Causal relations Regression analysis Advertising Context (language use) 02 engineering and technology Test (assessment) Politics 020901 industrial engineering & automation General election 0202 electrical engineering electronic engineering information engineering Computer science and engineering [Engineering] 020201 artificial intelligence & image processing Sociology Political Learning Software Multinomial logistic regression |
| Zdroj: | Applied Soft Computing. 97:106743 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2020.106743 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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