Joint Modelling of Emotion and Abusive Language Detection
By Santhosh Rajamanickam, Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova. 2020
61 citations recorded by the source corpus.
The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online. Aiming to tackle this problem, the natural language processing (NLP) community has experimented with a range of techniques for abuse detection. While achieving substantial success, these methods have so far only focused on modelling the linguistic properties of the comments and the online communities of users, disregarding the emotional state of the users and how this might affect their language. The latter is, however, inextricably linked to abusive behaviour. In this paper, we present the first joint model of emotion and abusive language detection, experimenting in a multi-task learning framework that allows one task to inform the other. Our results demonstrate that incorporating affective features leads to significant improvements in abuse detection performance across datasets.
Subfields: Artificial Intelligence, Information Systems, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ekaterina Shutova
- Helen Yannakoudakis
- Pushkar Mishra
- Endang Wahyu Pamungkas
- Viviana Patti
- Valerio Basile
- Ves Stoyanov
- Fabrizio Silvestri
In Information Systems
- Fabrizio Silvestri
- Alon Y. Halevy
- Viviana Patti
- Patrick Pantel
- Marzieh Saeidi
- Helen Yannakoudakis
- Ekaterina Shutova
- Pushkar Mishra
In Natural Language Processing
- Endang Wahyu Pamungkas
- Ekaterina Shutova
- Helen Yannakoudakis
- Pushkar Mishra
- Viviana Patti
- Valerio Basile
- Ves Stoyanov
- Fabrizio Silvestri
Citations in the corpus
- A Multimodal Framework for the Detection of Hateful Memes (2020, background): “[27] showed that multi-task learning with emotion detection can lead to significant improvements in abusive language detection.”
- Towards multidomain and multilingual abusive language detection: a survey (2021, background): “For example, some studies highlighted the importance of emotion information in abusive language detection task [109, 119].”
- Tackling Online Abuse: A Survey of Automated Abuse Detection Methods (2019, background): “Rajamanickam et al. (2020) demonstrated that jointly learning over emotion clas-”
- Preserving integrity in online social networks (2020, background): “consider the problem of adding affective features to the task of predicting abusive language [103].”