Beteiligte: | |
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In: | Global Media and Communication, 15, 2019, 3, S. 275-283 |
veröffentlicht: |
SAGE Publications
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Medientyp: | Artikel, E-Artikel |
Umfang: | 275-283 |
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ISSN: |
1742-7665
1742-7673 |
DOI: | 10.1177/1742766519872780 |
veröffentlicht in: | Global Media and Communication |
Sprache: | Englisch |
Schlagwörter: | |
Kollektion: | SAGE Publications (CrossRef) |
<jats:p> A wide variety of social media platforms have become integral to contemporary forms of social engagement, including mass protests. Twitter is considered specifically indicative of public attitudes in this regard. This study attempts to examine the feasibility of using Twitter sentiment analysis to predict the 2014 revolution in Ukraine. Tweets representing public opinion are clustered by means of the ‘StreamKM++’ algorithm into three classes (likely, neutral and unlikely). The resulting prediction model for the three classes (using Naïve Bayes) was 96.75 per cent. As such, this study offers a promising way to perform an online prediction of social movements. </jats:p> |