Strategies and Influence of Social Bots in a 2017 German state election - A case study on Twitter

Brachten et al. 2017, Hobart, Australia

Social Bots in a 2017 German state election

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Strategies and Influence of Social Bots in a 2017 German state election – A case study on Twitter

Florian Brachten University of Duisburg-Essen Duisburg, Germany Email: florian.brachten@uni-due.de Stefan Stieglitz University of Duisburg-Essen Duisburg, Germany Email: stefan.stieglitz@uni-due.de Lennart Hofeditz University of Duisburg-Essen Duisburg, Germany Email: lennart.hofeditz@stud.uni-due.de Katharina Kloppenborg University of Duisburg-Essen Duisburg, Germany Email: katharina.kloppenborg@stud.uni-due.de Annette Reimann University of Duisburg-Essen Duisburg, Germany Email: annette.reimann@stud.uni-due.de

Abstract As social media has permeated large parts of the population it simultaneously has become a way to reach many people e.g. with political messages. One way to efficiently reach those people is the application of automated computer programs that aim to simulate human behaviour - so called social bots. These bots are thought to be able to potentially influence users’ opinion about a topic. To gain insight in the use of these bots in the run-up to the German Bundestag elections, we collected a dataset from Twitter consisting of tweets regarding a German state election in May 2017. The strategies and influence of social bots were analysed based on relevant features and network visualization. 61 social bots were identified. Possibly due to the concentration on German language as well as the elections regionality, identified bots showed no signs of collective political strategies and low to none influence. Implications are discussed. Keywords: political bots; social bots; social media; Twitter; state election

Australasian Conference on Information Systems

Brachten et al. 2017, Hobart, Australia

Social Bots in a 2017 German state election

2 1 Introduction Social media have gained importance in political communication over the last years. People as well as political actors use social media such as Twitter to debate political topics or to conduct political online campaigns (Yang et al. 2016). Twitters retweet system combined with its public nature strongly adds to the diffusion of information (Stieglitz and Dang-Xuan 2012). However, social media like Twitter also attract people who aim to abuse their functionalities and apply their potential as an efficient way to spread messages to a large audience with little effort (Rinke 2016). One potential danger in this context is that users could attempt to manipulate public opinion or to disrupt political communication. Within social networks such as Twitter, an effective tool for accomplishing this feat is the use of so called social bots (Woolley 2016).
Social bots are automated social media accounts designed to mimic human behavior (Abokhodair et al. 2015; Ferrara et al. 2016; Freitas et al. 2015). Through the simulation of human behaviour they are at first glance not easily recognizable as artificial accounts (Ferrara et al. 2016). This in turn could lead to human users misjudging the importance of the messages spread by such accounts eventually leading to being influenced in favour of the messages at display. The accounts differ on their level of sophistication with low-level-accounts, merely aggregating information from websites and using it to produce simple messages, e.g. on Twitter. A more sophisticated social bot on the other hand can be conversational and aim at passing as a human (Abokhodair et al. 2015).
The application of such accounts has been observed in several political contexts such as the Brexit debate in 2016 or the US presidential election in 2016 where social bots were responsible for roughly one-fifth of the conversation on Twitter (Howard and Kollanyi 2016). They potentially influenced users’ opinion about the election as one candidate seemed to have more support than the other (Bessi and Ferrara 2016; Kollanyi et al. 2016). Kindled by observations of the use of these accounts in important votes, a debate considering the use of such accounts in the state election in 2017 in the most populous German state of North-Rhine Westphalia (NRW) has been a topic in the media and politics of the country (Rinke 2016). Driven by the ongoing debate about potential dangers of social bots and by the statement of the right wing populist party Alternative für Deutschland (Alternative for Germany - AfD) to potentially use social bots, all other major parties officially refrained from using social bots during their campaigns (“Das sagen die NRW-Parteien zu Social Bots” 2016). Accordingly, social bots in support of the right-winged AfD have been identified on Facebook by the popular media (Bender and Oppong 2017).
Besides the detection of social bots themselves, another important part in research is the detection and identifi

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