Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election
Abstract This paper proposes an analysis of political homophily among Twitter users during the 2016 American Presidential Election. We collected 4.9 million tweets of 18,450 users and their contact network from August 2016 to November 2016. We defined six user classes regarding their sentiment towar...
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| Online Access: | http://link.springer.com/article/10.1186/s13174-018-0089-0 |
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doaj-32d30388799e4b9d8003925270a77e2c2020-11-25T01:02:14ZengSpringerOpenJournal of Internet Services and Applications1867-48281869-02382018-09-019111510.1186/s13174-018-0089-0Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential electionJosemar A. Caetano0Hélder S. Lima1Mateus F. Santos2Humberto T. Marques-Neto3Department of Computer Science, Pontifical Catholic University of Minas Gerais (PUC Minas)Department of Computer Science, Pontifical Catholic University of Minas Gerais (PUC Minas)Department of Computer Science, Pontifical Catholic University of Minas Gerais (PUC Minas)Department of Computer Science, Pontifical Catholic University of Minas Gerais (PUC Minas)Abstract This paper proposes an analysis of political homophily among Twitter users during the 2016 American Presidential Election. We collected 4.9 million tweets of 18,450 users and their contact network from August 2016 to November 2016. We defined six user classes regarding their sentiment towards Donald Trump and Hillary Clinton: whatever, Trump supporter, Hillary supporter, positive, neutral, and negative. Next, we analyzed their political homophily in three scenarios. Firstly, we analyzed the Twitter follow, mention and retweet connections either unidirectional and reciprocal. In the second scenario, we analyzed multiplex connections, and in the third one, we analyzed friendships with similar speeches. Our results showed that negative users, users supporting Trump, and users supporting Hillary had homophily in all analyzed scenarios. We also found out that the homophily level increase when there are reciprocal connections, similar speeches, or multiplex connections.http://link.springer.com/article/10.1186/s13174-018-0089-0InternetOnline social networksSentiment analysisHomophily |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Josemar A. Caetano Hélder S. Lima Mateus F. Santos Humberto T. Marques-Neto |
| spellingShingle |
Josemar A. Caetano Hélder S. Lima Mateus F. Santos Humberto T. Marques-Neto Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election Journal of Internet Services and Applications Internet Online social networks Sentiment analysis Homophily |
| author_facet |
Josemar A. Caetano Hélder S. Lima Mateus F. Santos Humberto T. Marques-Neto |
| author_sort |
Josemar A. Caetano |
| title |
Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election |
| title_short |
Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election |
| title_full |
Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election |
| title_fullStr |
Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election |
| title_full_unstemmed |
Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election |
| title_sort |
using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 american presidential election |
| publisher |
SpringerOpen |
| series |
Journal of Internet Services and Applications |
| issn |
1867-4828 1869-0238 |
| publishDate |
2018-09-01 |
| description |
Abstract This paper proposes an analysis of political homophily among Twitter users during the 2016 American Presidential Election. We collected 4.9 million tweets of 18,450 users and their contact network from August 2016 to November 2016. We defined six user classes regarding their sentiment towards Donald Trump and Hillary Clinton: whatever, Trump supporter, Hillary supporter, positive, neutral, and negative. Next, we analyzed their political homophily in three scenarios. Firstly, we analyzed the Twitter follow, mention and retweet connections either unidirectional and reciprocal. In the second scenario, we analyzed multiplex connections, and in the third one, we analyzed friendships with similar speeches. Our results showed that negative users, users supporting Trump, and users supporting Hillary had homophily in all analyzed scenarios. We also found out that the homophily level increase when there are reciprocal connections, similar speeches, or multiplex connections. |
| topic |
Internet Online social networks Sentiment analysis Homophily |
| url |
http://link.springer.com/article/10.1186/s13174-018-0089-0 |
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