Hate speech’s double damage: A semi-automated approach toward direct and indirect targets





hate speech online, user comments, comment moderation, platforms, Twitter, YouTube, online journalism, Netzwerkdurchsetzungsgesetz, NetzDG, online public sphere


Democracies around the world have been facing increasing challenges with hate speech online as it contributes to a tense and thus less discursive public sphere. In that, hate speech online targets free speech both directly and indirectly, through harassments and explicit harm as well as by informing a vicious environment of irrationality, misrepresentation, or disrespect. Consequently, platforms have implemented varying means of comment-moderation techniques, depending both on policy regulations and on the quantity and quality of hate speech online. This study seeks to provide descriptive measures between direct and indirect targets in light of different incentives and practices of moderation on both social media and news outlets. Based on three distinct samples from German Twitter, YouTube, and a set of four news outlets, it applies semi-automated content analyses using a set of five cross-sample classifiers. Thereby, the largest amounts of visible hate speech online depict rather implicit devaluations of ideas or behavior. More explicit forms of hate speech online, such as insult, slander, or vulgarity, are only rarely observable and accumulate around certain events (Twitter) or single videos (YouTube). Moreover, while hate speech on Twitter and YouTube tends to target particular groups or individuals, hate speech below news articles shows a stronger focus on debates. Potential reasons and implications are discussed in light of political and legal efforts in Germany.

Author Biographies

Mario Haim, LMU Munich

Chair of Communication Science, especially Computational Communication Research, Department of Media and Communication

Elisa Hoven, University of Leipzig

Chair of Criminal Law, especially Criminal Procedure Law and Media Criminal Law




How to Cite

Haim, M., & Hoven, E. (2022). Hate speech’s double damage: A semi-automated approach toward direct and indirect targets. Journal of Quantitative Description: Digital Media , 2. https://doi.org/10.51685/jqd.2022.009