JOURNAL ARTICLE

"Die Geschichte unseres Landes ist so viel mehr als nur der Zweite Weltkrieg": German Cultural History Meets Transnational Genre Narratives in How to Sell Drugs Online (Fast).

  • Published In: Seminar -- A Journal of Germanic Studies, 2025, v. 61, n. 4. P. 239 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Schaper, Benjamin 3 of 3

Abstract

This article analyzes Netflix’s original German series *How to Sell Drugs Online (Fast)* (2019–) as a case study of German streaming television’s transnational turn in the convergence era. The series blends internationally recognizable genre narratives—such as the US computer nerd and crime genres—with German cultural history, including references to canonical literature like Goethe’s *Faust* and Wedekind’s *Frühlings Erwachen*, to appeal to global audiences while engaging with contemporary German societal debates. It employs narrative complexity, intertextuality, and self-reflexivity, notably through its framing devices and meta-commentary on social media and streaming platforms, to challenge traditional stereotypes and explore identity performance. Set in a generic small German town, the show critiques Germany’s digital lag and generational conflicts, positioning itself within both German media traditions and the global streaming landscape. Overall, *How to Sell Drugs Online (Fast)* exemplifies how German streaming productions negotiate local cultural specificity and international genre conventions to reach diverse audiences in the digital age.

Additional Information

  • Source:Seminar -- A Journal of Germanic Studies. 2025/11, Vol. 61, Issue 4, p239
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:0037-1939
  • DOI:10.3138/seminar.61.4.2
  • Accession Number:190435072
  • Copyright Statement:Copyright of Seminar -- A Journal of Germanic Studies is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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