Using sentiment analysis to predict opinion inversion in Tweets of political communication

Abstract Social media networks have become an essential tool for sharing information in political discourse. Recent studies examining opinion diffusion have highlighted that some users may invert a message's content before disseminating it, propagating a contrasting view relative to that of the...

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Autores principales:	Yogev Matalon, Ofir Magdaci, Adam Almozlino, Dan Yamin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1cb0ef6aa1714a5bb2a1f0c3e4bef89a
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spelling	oai:doaj.org-article:1cb0ef6aa1714a5bb2a1f0c3e4bef89a2021-12-02T14:25:15ZUsing sentiment analysis to predict opinion inversion in Tweets of political communication10.1038/s41598-021-86510-w2045-2322https://doaj.org/article/1cb0ef6aa1714a5bb2a1f0c3e4bef89a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86510-whttps://doaj.org/toc/2045-2322Abstract Social media networks have become an essential tool for sharing information in political discourse. Recent studies examining opinion diffusion have highlighted that some users may invert a message's content before disseminating it, propagating a contrasting view relative to that of the original author. Using politically-oriented discourse related to Israel with focus on the Israeli–Palestinian conflict, we explored this Opinion Inversion (O.I.) phenomenon. From a corpus of approximately 716,000 relevant Tweets, we identified 7147 Source–Quote pairs. These Source–Quote pairs accounted for 69% of the total volume of the corpus. Using a Random Forest model based on the Natural Language Processing features of the Source text and user attributes, we could predict whether a Source will undergo O.I. upon retweet with an ROC-AUC of 0.83. We found that roughly 80% of the factors that explain O.I. are associated with the original message's sentiment towards the conflict. In addition, we identified pairs comprised of Quotes related to the domain while their Sources were unrelated to the domain. These Quotes, which accounted for 14% of the Source–Quote pairs, maintained similar sentiment levels as the Source. Our case study underscores that O.I. plays an important role in political communication on social media. Nevertheless, O.I. can be predicted in advance using simple artificial intelligence tools and that prediction might be used to optimize content propagation.Yogev MatalonOfir MagdaciAdam AlmozlinoDan YaminNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yogev Matalon Ofir Magdaci Adam Almozlino Dan Yamin Using sentiment analysis to predict opinion inversion in Tweets of political communication
description	Abstract Social media networks have become an essential tool for sharing information in political discourse. Recent studies examining opinion diffusion have highlighted that some users may invert a message's content before disseminating it, propagating a contrasting view relative to that of the original author. Using politically-oriented discourse related to Israel with focus on the Israeli–Palestinian conflict, we explored this Opinion Inversion (O.I.) phenomenon. From a corpus of approximately 716,000 relevant Tweets, we identified 7147 Source–Quote pairs. These Source–Quote pairs accounted for 69% of the total volume of the corpus. Using a Random Forest model based on the Natural Language Processing features of the Source text and user attributes, we could predict whether a Source will undergo O.I. upon retweet with an ROC-AUC of 0.83. We found that roughly 80% of the factors that explain O.I. are associated with the original message's sentiment towards the conflict. In addition, we identified pairs comprised of Quotes related to the domain while their Sources were unrelated to the domain. These Quotes, which accounted for 14% of the Source–Quote pairs, maintained similar sentiment levels as the Source. Our case study underscores that O.I. plays an important role in political communication on social media. Nevertheless, O.I. can be predicted in advance using simple artificial intelligence tools and that prediction might be used to optimize content propagation.
format	article
author	Yogev Matalon Ofir Magdaci Adam Almozlino Dan Yamin
author_facet	Yogev Matalon Ofir Magdaci Adam Almozlino Dan Yamin
author_sort	Yogev Matalon
title	Using sentiment analysis to predict opinion inversion in Tweets of political communication
title_short	Using sentiment analysis to predict opinion inversion in Tweets of political communication
title_full	Using sentiment analysis to predict opinion inversion in Tweets of political communication
title_fullStr	Using sentiment analysis to predict opinion inversion in Tweets of political communication
title_full_unstemmed	Using sentiment analysis to predict opinion inversion in Tweets of political communication
title_sort	using sentiment analysis to predict opinion inversion in tweets of political communication
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1cb0ef6aa1714a5bb2a1f0c3e4bef89a
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Using sentiment analysis to predict opinion inversion in Tweets of political communication

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