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AbstractIn recent years an increasing number of analyses of offensive language has been published, however, dealing mainly with the automatic detection and classification of isolated instances. In this paper we …...

AbstractThis system paper is a description of the system submitted to “SemEval-2019 Task 6”, where we had to detect offensive language in Twitter. There were two specific target audiences, immigrants …...

AbstractThis paper describes the Duluth systems that participated in SemEval–2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval–2020). We participated in the three English language tasks. Our systems …...

AbstractAbusive language detection is an unsolved and challenging problem for the NLP community. Recent literature suggests various approaches to distinguish between different language phenomena (e.g., hate speech vs. cyberbullying vs. …...

AbstractThe presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern society. Given the enormous amount of content created every …...

In this paper, we analyze the interplay be- tween the use of offensive language and men- tal health. We acquired publicly available datasets created for offensive language identi- fication and …...

AbstractThis paper describes our system used in the SemEval-2023 Task 11 Learning With Disagreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive …...

AbstractOnline discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offensive language and the …...