Abusive Language Recognition in R ussian (2021)

Abstract: Abusive phenomena are commonplace in language on the web. The scope of recognizing abusive language is broad, covering many behaviors and forms of expression. This work addresses automatic detection of abusive language in Russian. The lexical, grammatical and morphological diversity of Russian language present potential difficulties for this task, which is addressed using a variety of machine learning approaches. Finally, competitive performance is reached over multiple domains for this investigation into automatic detection of abusive language in Russian.

## introduction unfortunately, hate speech and abusive language are prevalent on the internet @xcite , often creating an aggressive environment for users. this can include cyber-bullying or threats towards individuals and groups. reducing this content is difficult: it is harmful for humans to moderate. 1 thus, there is a critical need for abusive language recognition systems, which would help social networks and forums filter abusive language. moreover, with platforms taking increased control over which content to surface, automatic abuse recognition is more important than ever. one problem arises when the subjectivity of the matter is considered. abusive language is hard for humans to recognize universally @xcite . this indicates that the collection and labeling of data should be thorough and objective, which could be reached through e.g. large-scale crowd-sourced data annotation @xcite . nlp research in the area is nascent, with existing solutions oriented mostly towards english language (vidgen and derczynski, 2020), which, despite sometimes being mistakenly considered as "universal" @xcite , is very different grammatically and lexically from many languages, especially those using non-latin characters (e.g. russian, japanese etc). this paper addresses abusive language detection in russian. one issue with recognition of abusive language in russian is the limited number of sources of labeled data relative to english @xcite @xcite . thus, the collection and labeling of data presents an additional challenge, and we present both dataset and models. ## abusive language definition in this case, we use the olid annotation definition of abusive language @xcite . this covers profanity, and targeted and untargeted insults and threats, against both groups and individuals. specifically, in accordance this scheme, we consider the use of racial and other group-targeted slurs abusive. ## conclusion this paper presented data, models and experiments for abusive language detection in russian. by choosing the right preprocessing techniques and language-specific feature selection it is possible to achieve state-of-the-art performance on par with best-performing english language models, even using a simple svm model. this indicates that, given sufficient diversity of data, abusive language detection solutions can be rapidly developed for new languages. the code and data for this research are publicly available at: https://github.com/sariellee/ russan-hate-speech-recognition