When I opened the door to the administration of the special committee on the 2016 Berlin attack for the first time in 2018, I glance at 4,500 folders with 690,000 pages of investigation records, emails, and police files. This first glimpse dates back two years when I started working for two members of parliament, jointly scrutinizing the most severe Islamist attack in Germany. Today, I am sitting in front of an air-gapped computer, fanning it thoroughly to prevent it from overheating. The machine runs my NLP-based analysis of all documents mapping the social network around the terrorist. The nebulous amount of files indicates how much (more) data security agencies have to processes in a digitised democracy. And: that parliamentary control over authorities require automated data analysis to persist in future.
Both insights – the challenge of digitization for security authorities, as well as the effective control over their work – typify my academic and professional interest: How should domestic security work in the digitised democracy?
Studying Public Policy at Columbia University's School of International and Public Administration (SIPA) in New York City and the Hertie School in Berlin, as well as working as an advisor to public players in the German security sector, I understood that the intersection of security policy, data science and policy implementation is a pivotal combination to address my questions. Inspired by my studies, freelance projects, and practical experiences, I wish to make the digitised democracy safer – without restricting freedom or curtailing the rule of law.
The network shows Neonazi communication on a recently leaked Discord server. Initially design to facilite online gaming, the communication infrastructure has been corrupted to share fascist ideology. Guilds in Discord represent an isolated collection of users and channels, and are often referred to as "servers". Users can participate in several guilds and communicate within channels by exchanging messages.
Read some of the latest pieces of opinion. Triggered by course-related questions and issues popping up in every day policy work.
R- and Python-powered quantitative research. As this is preliminary, conclusions should be handled with care - like always!
Miscellaneous texts about policy issues. Sometimes more analytical, sometimes more normative, sometimes just something.