Data-driven Analysis of Hate Speech on German Twitter and the Effects of Regulation

Data-driven Analysis of Hate Speech on German Twitter and the Effects of Regulation

Period: 01.01.2023 – 30.06.2025

Social media have lately become one of the primary information channels for many individuals, which was exacerbated during the recent pandemic. As such, social media is a major example of the ambivalence of digitization: On the one hand, social media provide new opportunities for social interactions and political participation. On the other hand, they facilitate the dissemination of extremist thoughts and aggressive or harassing contents.

In order to reduce the dissemination of online hate speech, the German government implemented the Network Enforcement Act (NetzDG) in October 2017. This law obliges social platforms with more than 2 million users in Germany to delete posts and comments containing clearly hateful and insulting content within 24 hours after they have been reported.
While Germany was the first country to implement a law for regulating user generated content (UGC), other countries and the European Commission also started to regulate UGC in the meantime, mostly with similar approaches. This project investigates the effectiveness of such regulation by answering the research question if the Network Enforcement Act is effective in lowering the prevalence of hateful content on social networks in Germany?

For addressing this research question, we will conduct empirical studies on one of the world's largest social networks, i.e., Twitter. A data-based understanding of the prevalence of online hate speech and the impact of the NetzDG is crucial for establishing guidelines for developing future successful regulation of UGC in the EU and other countries.

In a preceding project, we compared the prevalence of hateful content in migration-related tweets in the German and Austrian Twittersphere before and after the introduction of the law in Germany and found a significant decrease in the amount of severely toxic tweets and tweets containing identity attacks. In this project, we aim at validating the results described above by running different econometric specifications. Furthermore, we plan on extending the analysis by a sophisticated and all-encompassing measure of online hate speech. To that end, we want to develop a tool for automated hate speech classification, which could be adapted to other settings and languages for a broad application of the project outcomes.
Additionally, we will address recent developments that receive large media echo. As such, the military conflict in Ukraine challenges social media platforms to introduce governing rules for addressing the problem of hate speech in the context of an ongoing military conflict. Twitter responded to this challenge by introducing a new policy. Further, after Twitter was bought by Elon Musk, there have been radical changes in the company, which may also affect its handling of hate speech. Furthermore, when looking at topics which may involve hate speech, we will also look into recent emerging topics with a high potential for polarization, such as e.g. the above-mentioned war in Ukraine and climate activism.

Project members

Raphaela Andres

Raphaela Andres


To the profile
Cooperation partner
Universität Mannheim, Mannheim, DE // Erasmus-Universität Rotterdam, Rotterdam, DE


Researcher, Raphaela Andres
Telefon +49 (0)621 1235-198