Walking Through Twitter: Sampling a Language-Based Follow Network of Influential Twitter Accounts

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.

Mapping a Dark Space: Challenges in Sampling and Classifying Non- Institutionalized Actors on Telegram

“…Some studies only include channels and exclude chat groups in their sampling iterations (Su et al, 2022;Teo & Fu, 2021;Urman & Katz, 2022b). To detect the most influential accounts of the German Twittersphere in a resource-efficient way, Münch and colleagues apply the rank degree method, which only includes the most influential accounts for the subsequent sampling iteration (Münch et al, 2021). To further constrain the sampling process, they propose automated language detection, which could be applied when the target population is rendered by language.…”

“…One hindrance to the evaluation of a stop condition is the lack of a recognized reference dataset or benchmark represent ing an adequate population; the closest contender is the Pushshift (Baumgartner et al, 2020) dataset. With such a regularly updated dataset, a stop condition could be defined by comparing various metrics (Münch et al, 2021) with the sampled network. Another method is network saturation, in which the search is terminated if only a few unknown channels are found in the last iteration (La Morgia et al, 2021;Buehling & Heft, 2023).…”

Census of Twitter users: Scraping and describing the national network of South Korea

“…However, later, Twitter conducted a series of data protection changes to their developer APIs. Those properties, including geotags, user time zone, and the interface language used on Twitter, are all inaccessible by now [ 11 ].…”

“…Bruns & Enli collected the entire national Twittersphere of Norway based on the Norwegian language of user profile [ 12 ]. Bruns and others scraped the German Twitter network dataset TrISMA [ 13 ] and later the dataset served as a ground truth for the so-called rank degree sampling method [ 11 ]. Recently, Munch and Rossi compared two national Twitter networks, Italian and German, based on the language detection of user profile [ 14 ].…”