Twitter continuously tightens the access to its data via the publicly accessible, cost-free standard APIs. This especially applies to the follow network. In light of this, we successfully modified a network sampling method to work efficiently with the Twitter standard API in order to retrieve the most central and influential accounts of a language-based Twitter follow network: the German Twittersphere. We provide evidence that the method is able to approximate a set of the top 1% to 10% of influential accounts in the German Twittersphere in terms of activity, follower numbers, coverage, and reach. Furthermore, we demonstrate the usefulness of these data by presenting the first overview of topical communities within the German Twittersphere and their network structure. The presented data mining method opens up further avenues of enquiry, such as the collection and comparison of language-based Twitterspheres other than the German one, its further development for the collection of follow networks around certain topics or accounts of interest, and its application to other online social networks and platforms in conjunction with concepts such as agenda setting and opinion leadership.
The growth of online platforms is accompanied by the increasing use of automated agents. Despite being discussed primarily in the context of opinion manipulation, agents play diverse roles within platform ecosystems that raises the need for governance approaches that go beyond policing agents’ unwanted behaviour. To provide a more nuanced assessment of agent governance, we introduce an analytical framework that distinguishes between different aspects and forms of governance. We then apply it to explore how agents are governed across nine platforms. Our observations show that despite acknowledging diverse roles of agents, platforms tend to focus on governing selected forms of their misuse. We also observe differences in governance approaches used by platforms, in particular when it comes to the agent rights/obligations and transparency of policing mechanisms. These observations highlight the necessity of advancing the algorithmic governance research agenda and developing a generalizable normative framework for agent governance.
Global political developments – such as Brexit, climate change, or forced migration – are entangled with communication that transcends national publics. Meanwhile, the EU’s integrity suffers, also due to polarised online discourses, which are sometimes actively manipulated. Therefore, an overview of online communication beyond language barriers is essential. However, whether and how online media create a global space that sustains deliberation of national and global interests by citizens, remains understudied. We approach this problem by exploring relations between the Italian and German Twittersphere, while asking: 1) What is the macrostructure of this bilingual network? 2) Are there bridges between these language communities in the form of single accounts and how can they be described? 3) Are there bridges in the form of groups and what are they tweeting about? We build on an innovative network crawling strategy for language-based Twitter follow networks. We developed it further to combine strengths of rank degree, snowball, and forest fire sampling. Thereby, we collect a network sample of the most central accounts in the Italian-German Twittersphere. Preliminary results suggest a bridging quality of soccer and connections between political clusters of both languages by EU politicians. Furthermore, larger network clusters connect mainly with one linguistic domain while smaller communities show a bridging behaviour. The final paper will present results of months of data collection, focusing on the relation between topics discussed within clusters and their connectivity. While it focuses on the German-Italian Twittersphere, our methods open up new avenues of enquiry regarding multi-language public spheres.
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