Ruddit: Norms of Offensiveness for English Reddit Comments

Ruddit: Norms of Offensiveness for English Reddit Comments Rishav Hada1,∗, Sohi Sudhir1,∗, Pushkar Mishra2, Helen Yannakoudakis3, Saif M. Mohammad4, Ekaterina Shutova1 1ILLC, University of Amsterdam 2Facebook AI, London 3Dept. of Informatics, King’s College London 4National Research Council Canada rishavhada@gmail.com, sohigre@gmail.com, pushkarmishra@fb.com, helen.yannakoudakis@kcl.ac.uk, saif.mohammad@nrc-cnrc.gc.ca, e.shutova@uva.nl Abstract Warning: This paper contains comments that may be offensive or upsetting. On social media platforms, hateful and offen- sive language negatively impact the mental well-being of users and the participation of peo- ple from diverse backgrounds. Automatic meth- ods to detect offensive language have largely relied on datasets with categorical labels. How- ever, comments can vary in their degree of of- fensiveness. We create the first dataset of En- glish language Reddit comments that has fine- grained, real-valued scores between -1 (maxi- mally supportive) and 1 (maximally offensive). The dataset was annotated using Best–Worst Scaling, a form of comparative annotation that has been shown to alleviate known biases of using rating scales. We show that the method produces highly reliable offensiveness scores. Finally, we evaluate the ability of widely-used neural models to predict offensiveness scores on this new dataset. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 2700–2717 August 1–6, 2021. ©2021 Association for Computational Linguistics 1 Introduction Social media platforms serve as a medium for ex- change of ideas on a range of topics, from the per- sonal to the political. This exchange can, however, be disrupted by offensive or hateful language. Such language is pervasive online (Statista, 2020b), and exposure to it may have numerous negative con- sequences for the victim’s mental health (Munro, 2011). Automated offensive language detection has thus been gaining interest in the NLP community, as a promising direction to better understand the nature and spread of such content. There are several challenges in the automatic detection of offensive language (Wiedemann et al., 2018). The NLP community has adopted various definitions for offensive language, classifying it into specific categories. For example, Waseem and ∗Both authors contributed equally. Hovy (2016) classified comments as racist, sex- ist, neither; Davidson et al. (2017) as hate-speech, offensive but not hate-speech, neither offensive nor hate-speech and Founta et al. (2018) as abu- sive, hateful, normal, spam. Schmidt and Wiegand (2017); Fortuna and Nunes (2018); Mishra et al. (2019); Kiritchenko and Nejadgholi (2020) summa- rize the different definitions. However, these cat- egories have significant overlaps with each other, creating ill-defined boundaries, thus introducing ambiguity and annotation inconsistency (Founta et al., 2018). A further challenge is that after en- countering several highly offensive comments, an annotator might find subsequent moderately offen- sive comments to not be offensive (de-sensitization) (Kurrek et al., 2020; Soral et al., 2018). At the same time, existing approaches do not take into account that comments can be offensive to a different degree. Knowing the degree of offen- siveness of a comment has practical implications, when taking action against inappropriate behaviour online, as it allows for a more fine-grained …

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