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.
In the light of recent events (such as the 2016 US presidential elections and the 2016 Brexit referendum), numerous authors reported on the misuse of social media channels aiming to manipulate the voters' opinions (cf. @cite_31 @cite_22 @cite_62 ). However, such a misuse has already been reported much earlier. For example, in @cite_47 discuss the issue of political abuse by studying the spread of misinformation. The authors consider mood scores based on Google's Profile of Mood States (calm, alert, sure, vital, kind, happy) (see @cite_20 ) and basic properties of the hashtag network and user-mention (i.e. ) network topology to automatically classify messages as truthful or fake. Another study by @cite_68 investigates the characteristics of the spread of misinformation concerning Ebola by applying epidemiological modeling (see also @cite_27 ). Moreover, in @cite_57 Howard and Kollanyi report that bots have been used to amplify messages by raising their re-tweet count in order to influence the 2016 Brexit referendum. Even though Howard and Kollanyi identified only a small amount of bots participating in discussions about Brexit, those bots created a comparatively large amount of the overall content (about one third of all messages).
@cite_31 @cite_62 @cite_22 @cite_57 @cite_27 @cite_47 @cite_68 @cite_20
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Bots are social media accounts that automate interaction with other users, and they are active on the StrongerIn-Brexit conversation happening over Twitter. These automated scripts generate content through these platforms and then interact with people. Political bots are automated accounts that are particularly active on public policy issues, elections, and political crises. In this preliminary study on the use of political bots during the UK referendum on EU membership, we analyze the tweeting patterns for both human users and bots. We find that political bots have a small but strategic role in the referendum conversations: (1) the family of hashtags associated with the argument for leaving the EU dominates, (2) different perspectives on the issue utilize different levels of automation, and (3) less than 1 percent of sampled accounts generate almost a third of all the messages. The population dynamics underlying the diffusion of ideas hold many qualitative similarities to those involved in the spread of infections. In spite of much suggestive evidence this analogy is hardly ever quantified in useful ways. The standard benefit of modeling epidemics is the ability to estimate quantitatively population average parameters, such as interpersonal contact rates, incubation times, duration of infectious periods, etc. In most cases such quantities generalize naturally to the spread of ideas and provide a simple means of quantifying sociological and behavioral patterns. Here we apply several paradigmatic models of epidemics to empirical data on the advent and spread of Feynman diagrams through the theoretical physics communities of the USA, Japan, and the USSR in the period immediately after World War II. This test case has the advantage of having been studied historically in great detail, which allows validation of our results. We estimate the effectiveness of adoption of the idea in the three communities and find values for parameters reflecting both intentional social organization and long lifetimes for the idea. These features are probably general characteristics of the spread of ideas, but not of common epidemics. We study astroturf political campaigns on microblogging platforms: politically-motivated individuals and organizations that use multiple centrally-controlled accounts to create the appearance of widespread support for a candidate or opinion. We describe a machine learning framework that combines topological, content-based and crowdsourced features of information diffusion networks on Twitter to detect the early stages of viral spreading of political misinformation. We present promising preliminary results with better than 96 accuracy in the detection of astroturf content in the run-up to the 2010 U.S. midterm elections. A quantitative analysis of tweets during the Ebola crisis reveals that lies, half-truths, and rumors can spread just like true news. Behavioral finance researchers can apply computational methods to large-scale social media data to better understand and predict markets.