# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 1 Introduction  
### Background  
While the rapid growth of social media has brought people closer together, the amount of abuse language has grown at an alarming rate along with the number of users. The term abuse refers to all forms of expression that denigrate or offend an individual or group of people, including racism, sexism, personal attacks, harassment, cyberbullying, etc. [1], and abusive language also could be used on specific individuals or groups [2]. Abusive behavior online can lead to severe psychological consequences for its victims and hampers free speech. Automatic abuse detection can mine offensive language in a large number of online social comments, which is critical for avoiding psychological impact on victims, and thereby preventing hate crimes [3]. Therefore, how to detect abuse automatically becomes an important problem in the emotional computing field.  
In general, abuse detection can be viewed as an online social text classification task. With the continuous development of natural language processing (NLP) technology, abuse detection can be divided into three different stages. Some of the earliest studies used logistic regression models for automatic abuse detection by using a variety of different features, such as character-level and word-level n-gram features, syntactic features, linguistic features, and comment-embedding features [4, 5, 6, 7]. They represent traditional machine learning methods where the abuse detection results depend on the characteristics of a manual design. The second stage is a deep learning method represented by convolutional neural networks (CNNs) and recurrent neural networks (RNNs) [8]. Deep learning methods do not rely on manual design features and can automatically capture the context features of abused language through neural networks. Thirdly, with the emergence of large-scale pretrainedlanguage models, such as BERT [9] and HateBERT [10], the universality and transferability of knowledge in text have been further explored.  
However, due to the variety of linguistic traits, the automatic detection of abuse is still very challenging [11]. For example, [12] divides abuse into explicitness and directness. Abuse of the former type manifests itself in a direct way, perhaps in the form of certain abusive words, but the latter type may cover metaphors or analogies which may lead to some possible confusion and errors. Moreover, most of the existing methods focus on how to model the features of linguistic properties, without fully taking into account the influence of users' emotions and psychological states on their expressions. Psychological research has proved that abusive language and HateBERT are also inseparable from the speaker's emotional and psychological state [13]. In addition, some studies in other fields of affective computing have also confirmed the relationship between users' emotions and their psychological traits, such as personality detection [14]. Therefore, the main purpose of this paper is to answer the following question: Can effective emotion classification facilitates abuse detection? To this end, we propose a multitask learning (level) method combining emotion classification and abuse detection to verify the learning effect of abuse detection by taking user emotion classification as an auxiliary task.