Jisu Shin  Hoyun Song  Jong C. Park

10.5626/JOK.2022.49.11.948

abusive language detection implicitly abusive language out of distribution Social Media Toxicity

Implicitly abusive language detection is a difficult problem to solve due to diversity of expressions and absence of a clear definition. Previous studies have claimed that implicitly abusive language should be classified and defined in detail, accompanied by corresponding datasets. However, this is not only inefficient, but also hard to flexibly respond to language changes. Our work proposes an efficient and effective method that processes implicitly abusive language as Out-of-Distribution data for the first time. In our experiments, a model with the proposed method performed better than a general pre-trained model and lexicon-based models. We also performed sentiment analysis and a case study to analyze characteristics of implicitly abusive language in detail and differences between a general pre-trained model and our model.