  In this paper, we provide a systematic 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, sentiment analysis, as well as bot detection. Among other things, we
found that: a) the winner of the election (Alexander Van der Bellen) was
considerably more popular and influential on Twitter than his opponent, b) the
Twitter followers of Van der Bellen substantially participated in the spread of
misinformation about him, c) there was a clear polarization in terms of the
sentiments spread by Twitter followers of the two presidential candidates, d)
the in-degree and out-degree distributions of the underlying communication
network are heavy-tailed, and e) compared to other recent events, such as the
2016 Brexit referendum or the 2016 US presidential elections, only a very small
number of bots participated in the Twitter discussion on the 2016 Austrian
presidential election.
