In this paper, we provide a sentiment analysis of the Twitter discussion on the 2016 Austrian presidential elections. In particular, we extracted and analyzed a data-set consisting of 343645 Twitter messages related to the 2016 Austrian presidential elections. Our analysis combines methods from network science and sentiment analysis. Among other things, we found that: a) the winner of the election (Alexander Van der Bellen) predominantly sent tweets resulting in neutral sentiment scores, while his opponent (Norbert Hofer) preferred emotional messages (i.e. tweets resulting in positive or negative sentiment scores), b) negative information about both candidates continued spreading for a longer time compared to neutral and positive information, c) there was a clear polarization in terms of the sentiments spread by Twitter followers of the two presidential candidates, d) the winner of the election received considerably more likes and retweets, while his opponent received more replies, e) the Twitter followers of the winner substantially participated in the spread of misinformation about him.
Österreichische Systematik der Wissenschaftszweige (ÖFOS)
- 102015 Informationssysteme