I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language

Caselli, T., Basile, V., Mitrović, J., Kartozya, I. & Granitzer, M., 2020, p. 1-11. 12 p.

Research output: Contribution to conferencePaperAcademic

  • Tommaso Caselli
  • Valerio Basile
  • Jelena Mitrović
  • Inga Kartozya
  • Micheal Granitzer
Abusive 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. offensive language) and factors
(degree of explicitness and target) that may help to classify different abusive language phenomena. There are data sets that annotate the
target of abusive messages (i.e.OLID/OffensEval (Zampieri et al., 2019a)). However, there is a lack of data sets that take into account the
degree of explicitness. In this paper, we propose annotation guidelines to distinguish between explicit and implicit abuse in English and
apply them to OLID/OffensEval. The outcome is a newly created resource, AbuseEval v1.0, which aims to address some of the existing
issues in the annotation of offensive and abusive language (e.g., explicitness of the message, presence of a target, need of context, and
interaction across different phenomena)
Original languageEnglish
Number of pages12
Publication statusPublished - 2020
Event12th Language Resources and Evaluation Conference
: LREC 2020
- Marseille, France
Duration: 11-May-202016-May-2020


Conference12th Language Resources and Evaluation Conference
Internet address


12th Language Resources and Evaluation Conference
: LREC 2020


Marseille, France

Event: Conference

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