Toxic Bias: Perspective API Misreads German as More Toxic

Proprietary public APIs play a crucial and growing role as research tools among social scientists. Among such APIs, Google's machine learning-based Perspective API is extensively utilized for assessing the toxicity of social media messages, providing both an important resource for researchers and automatic content moderation. However, this paper exposes an important bias in Perspective API concerning German language text. Through an in-depth examination of several datasets, we uncover intrinsic language biases within the multilingual model of Perspective API. We find that the toxicity assessment of German content produces significantly higher toxicity levels than other languages. This finding is robust across various translations, topics, and data sources, and has significant consequences for both research and moderation strategies that rely on Perspective API. For instance, we show that, on average, four times more tweets and users would be moderated when using the German language compared to their English translation. Our findings point to broader risks associated with the widespread use of proprietary APIs within the computational social sciences.

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Publication Details

Journal
Proceedings of the International AAAI Conference on Web and Social Media
Published
2025-06-07
DOI
https://doi.org/10.1609/icwsm.v19i1.35876
Citations
5
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
12.40
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article

Toxic Bias: Perspective API Misreads German as More Toxic

Francesco Pierri, Gianluca Nogara, Petter Törnberg, Silvia Giordano et al.
5 citations
Proceedings of the International AAAI Conference on Web and Social Media
Natural Language Processing Techniques
12.40
article

Toxic Bias: Perspective API Misreads German as More Toxic

Francesco Pierri, Gianluca Nogara, Petter Törnberg, Silvia Giordano, Stefano Cresci, Luca Luceri
article en
5 citations

Abstract

Proprietary public APIs play a crucial and growing role as research tools among social scientists. Among such APIs, Google's machine learning-based Perspective API is extensively utilized for assessing the toxicity of social media messages, providing both an important resource for researchers and automatic content moderation. However, this paper exposes an important bias in Perspective API concerning German language text. Through an in-depth examination of several datasets, we uncover intrinsic language biases within the multilingual model of Perspective API. We find that the toxicity assessment of German content produces significantly higher toxicity levels than other languages. This finding is robust across various translations, topics, and data sources, and has significant consequences for both research and moderation strategies that rely on Perspective API. For instance, we show that, on average, four times more tweets and users would be moderated when using the German language compared to their English translation. Our findings point to broader risks associated with the widespread use of proprietary APIs within the computational social sciences.

Proceedings of the International AAAI Conference on Web and Social MediaVol. 19
University of Southern California (US), University of Applied Sciences and Arts of Southern Switzerland (CH), California Southern University (US), Institute of Informatics and Telematics (IT), University of Amsterdam (NL), Politecnico di Milano (IT)
Openalex Percentile: Top 2%
Natural Language Processing Techniques
12.40
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