This demonstration of a Twitter follow network sampler (Münch & Thies, 2019) addresses a common problem faced by online media researchers: data about follow or subscription networks are often hard to collect due to API restrictions. Despite the relevance of subscription networks as long-term infrastructures for content diffusion, they are often negated in favour of more easily collectable data, such as mention-, repost-, co-comment, or co-hashtag networks. We believe that this often is not done because of the common argument that follow-networks would be less suitable for the respective research question. This argument is often logically and empirically refutable. We believe that follow or subscription networks remain ignored, primarily, due to the inaccessibility of data via low-cost APIs, and, secondarily, due to the lack of tools to circumvent this obstacle with smart data-mining strategies.
Therefore, the development and dissemination of this kind of tools is relevant not only for large scale research regarding a networked public sphere or social cohesion, but also for smaller scale research, e.g., to get an impression of which long-term influences participants in a certain issue public experience. The presented tool has proven itself useful in a large-scale study regarding the macro-structure of the German Twittersphere, which is currently under review for publication.
For this tool demonstration we will present the results of our test study regarding the German Twittersphere to showcase the potential of RADICES for larger research projects and showcase the overall structure of the code and configuration file.
To illustrate the functionality of the tool during the tool demonstration, we will start to sample the most influential follow network connections around Twitter accounts that tweet about the ICA conference and present a visualisation of first results.