Social bots – partially or fully automated accounts on social media platforms – have not only been widely discussed, but have also entered political, media and research agendas. However, bot detection is not an exact science. Quantitative estimates of bot prevalence vary considerably and comparative research is rare. We show that findings on the prevalence and activity of bots on Twitter depend strongly on the methods used to identify automated accounts. We search for bots in political discourses on Twitter, using three different bot detection methods: Botometer, Tweetbotornot and “heavy automation”. We drew a sample of 122,884 unique user Twitter accounts that had produced 263,821 tweets contributing to five political discourses in five Western democracies. While all three bot detection methods classified accounts as bots in all our cases, the comparison shows that the three approaches produce very different results. We discuss why neither manual validation nor triangulation resolves the basic problems, and conclude that social scientists studying the influence of social bots on (political) communication and discourse dynamics should be careful with easy-to-use methods, and consider interdisciplinary research.

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Feministischer Aktivismus auf digitalen Plattformen geht einher mit Chancen ebenso wie mit neuen Gefahren - vom Vernetzungspotenzial und der Organisation feministischer Öffentlichkeiten auf der einen Seite, zu neuen Formen des Hasses gegen Aktivist*innen und des Ausschlusses bestimmter Personengruppen auf der anderen Seite. Mithilfe von Netzwerk- und Inhaltsanalysen untersucht diese Studie den deutschsprachigen #MeToo-Protest auf Twitter und geht der Frage nach, welche Akteure hier einflussreich und sichtbar waren und Twitter als Plattform für sich nutzen konnten. Es wird gezeigt, dass neben privaten Nutzer*innen vor allem traditionelle Massenmedien auf Twitter eine zentrale Rolle spielen. Gleichzeitig lässt sich innerhalb des #MeToo-Protests ein dichtes Netzwerk antifeministischer und rassistischer Stimmen finden, die strategisch für eigene Anliegen mobilisieren wollen. Daraus kann geschlossen werden, dass sich auch auf Twitter hierarchische Strukturen und qualitative Unterschiede der Vernetzung herausbilden, welche Barrieren für die öffentliche Artikulation feministischer Anliegen darstellen.

Certain varieties of feminism have become more popular, and so have anti-feminist reactions to it with both sides competing for visibility. However, the (gendered) interplay between feminist and anti-feminist counterpublics is still uncharted. At the same time, research in the field of feminist media studies is beginning to address questions of power inequalities $2 feminist publics on social media platforms. This study sheds light on the networked structure of the German-language #MeToo protest on Twitter in order to reveal who succeeded in becoming visible and influential in this digital protest and in order to show differences in networking practices among those involved. Analyzing the Twitter interaction network around #MeToo over a period of three month, we find that – as expected – this network consists of some highly connected hubs and a majority of nodes with only few connections. The most central nodes, only 1.1 percent of the Twitter users involved, account for 35 percent of interactions within the network. Applying qualitative and quantitative content analyses, this study shows that Twitter accounts of traditional news media play a central role in the #MeToo network from the very beginning, indicating that protest networks are less equal and horizontal than often assumed. At the same time, k-core decomposition reveals that most Twitter users in the network’s core published mostly racist and anti-feminist content, indicating that few but very loud and well-connected voices used the #MeToo protest to strategically mobilize against migration in Germany and Austria.