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 …
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 …
We present a study on bot detection and its interpretation by assessing the different types of automation that one of the most popular methods for bot detection, Botometer (https://botometer.iuni.iu.edu/), detects. The study is based on the first …
Social bots are undermining trust in social media. They spread low-credibility content (Shao et al., 2018), so-called fake news (Vosoughi, Roy, & Aral, 2018), and spam (Bruns et al., 2018). However, most research analyses data based on the active …
This conference contribution presents a new sampling approach for large follow networks of influential Twitter accounts, discusses first key results of its application to the German Twittersphere, and benchmarks their representativeness. Twitter …
Twitter is used by individuals, grassroots movements, and political and social elites to directly communicate to the public and influence opinion. The platform appears relatively accessible to researchers because the majority of accounts post …
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 …