Abstract
Abusive language is becoming a problematic issue for our society. The spread of messages that reinforce social and cultural intolerance could have dangerous effects in victims' life. State-of-the-art technologies are often effective on detecting explicit forms of abuse, leaving unidentified the utterances with very weak offensive language but a strong hurtful effect. Scholars have advanced theoretical and qualitative observations on specific indirect forms of abusive language that make it hard to be recognized automatically. In this work, we propose a battery of statistical and computational analyses able to support these considerations, with a focus on creative and cognitive aspects of the implicitness, in texts coming from different sources such as social media and news. We experiment with transformers, multi-task learning technique, and a set of linguistic features to reveal the elements involved in the implicit and explicit manifestations of abuses, providing a solid basis for computational applications.
| Original language | English |
|---|---|
| Pages (from-to) | 1516-1537 |
| Number of pages | 22 |
| Journal | Natural Language Engineering |
| Volume | 29 |
| Issue number | 6 |
| Early online date | 3 Aug 2022 |
| DOIs | |
| Publication status | Published - Nov 2023 |
Keywords
- Abusive language detection
- Figurative language
- Hate speech
- Linguistic analysis
- Stereotypes
ASJC Scopus subject areas
- Software
- Language and Linguistics
- Linguistics and Language
- Artificial Intelligence