No abstract

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.

No abstract

Social bots are undermining trust in social media. They spread low-credibility content, fake news, and spam. However, most research is based on bots that actively share links or keywords, rather than assessing the longer-term presence of bots as an integral part of platforms. To address this gap, we present what to our knowledge is the first study that assesses the prevalence, influence, and roles of automated accounts in a Twitter follow network on a national scale. This allows us to analyse the potential impact of bots beyond the context of single events and topics. To collect a follow network of the most central accounts in the German-speaking Twittersphere, we have adapted the rank-degree method, a graph exploration method that is able to identify the most influential spreaders within complex networks, as a data mining method using the cost-free standard Twitter API. To identify bots, we employ the Botometer API. Both methods combined allow us to localise bots within topical clusters, to estimate their potential influence, and to assess the roles of the most central bots. Our findings indicate that bots have only a low negative impact on the German-speaking Twittersphere. However, the most sophisticated bots will likely remain absent from our study, as false negatives. Similarly, trolls and semi-automated accounts necessitate further research. Our new sampling approach combined with Botometer is promising, for example, for Twitterspheres based on other languages. The study itself opens further avenues of enquiry, such as a long-term monitoring of automated accounts in the German Twittersphere.