From Toxicity in Online Comments to Incivility in American News: Proceed with Caution
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Abstract (arXiv)
The ability to quantify incivility online, in news and in congressional debates, is of great interest to political scientists. Computational tools for detecting online incivility for English are now fairly accessible and potentially could be applied more broadly. We test the Jigsaw Perspective API for its ability to detect the degree of incivility on a corpus that we developed, consisting of manual annotations of civility in American news. We demonstrate that toxicity models, as exemplified by Perspective, are inadequate for the analysis of incivility in news. We carry out error analysis that points to the need to develop methods to remove spurious correlations between words often mentioned in the news, especially identity descriptors and incivility. Without such improvements, applying Perspective or similar models on news is likely to lead to wrong conclusions, that are not aligned with the human perception of incivility.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 6 Feb 2021.
Builds on
10 of 24 references.
References · 10
- 2018Model Cards for Model Reportingcited by 1,068
- 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
- 2019ERASER: A Benchmark to Evaluate Rationalized NLP Modelscited by 523
- 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
- 2018Large Scale Crowdsourcing and Characterization of Twitter Abusive Behaviorcited by 265
- 2019Racial Bias in Hate Speech and Abusive Language Detection Datasetscited by 208
- 2020Social Biases in NLP Models as Barriers for Persons with Disabilitiescited by 182
- 2019Perturbation Sensitivity Analysis to Detect Unintended Model Biasescited by 74
- 2020Demoting Racial Bias in Hate Speech Detectioncited by 62
- 2018WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Communitycited by 16
Built on by
Citing papers per year: 2022: 3; 2023: 3; 2024: 0; 2025: 1; 2026: 1.
8 papers.
- 2022Listening to Affected Communities to Define Extreme Speech: Dataset and Experimentscited by 21
- 2022Analyzing Hate Speech Data along Racial, Gender and Intersectional Axescited by 7
- 2022The State of Profanity Obfuscation in Natural Language Processingcited by 4
- 2023Dimensions of Online Conflict: Towards Modeling Agonismcited by 2
- 2025Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Studycited by 2
- 2023Sociocultural knowledge is needed for selection of shots in hate speech detection taskscited by 1
- 2023Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!cited by 1
- 2026You Really Didn't Get That? Benchmarking Social Pragmatic Inference for Indirect and Playful Chinese Online Commentscited by 0
- 2026You Really Didn't Get That? Benchmarking Social Pragmatic Inference for Indirect and Playful Chinese Online Comments3 Sep 2026
- 2025Effectiveness of Counter-Speech against Abusive Content: A Multidimensional Annotation and Classification Study13 Jun 2025
- 2023Dimensions of Online Conflict: Towards Modeling Agonism6 Nov 2023
- 2023Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!12 Jul 2023
- 2023Sociocultural knowledge is needed for selection of shots in hate speech detection tasks4 Apr 2023
- 2022The State of Profanity Obfuscation in Natural Language Processing14 Oct 2022
- 2022Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes13 May 2022
- 2022Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments22 Mar 2022