Detect All Abuse! Toward Universal Abusive Language Detection Models (2020)

Abstract: Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years. Several previous works in online ALD focused on solving a single abusive language problem in a single domain, like Twitter, and have not been successfully transferable to the general ALD task or domain. In this paper, we introduce a new generic ALD framework, MACAS, which is capable of addressing several types of ALD tasks across different domains. Our generic framework covers multi-aspect abusive language embeddings that represent the target and content aspects of abusive language and applies a textual graph embedding that analyses the user’s linguistic behaviour. Then, we propose and use the cross-attention gate flow mechanism to embrace multiple aspects of abusive language. Quantitative and qualitative evaluation results show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD algorithms across seven ALD datasets covering multiple aspects of abusive language and different online community domains.

## introduction abusive language in online communities has become a significant societal problem @xcite and online abusive language detection (ald) aims to identify any type of insult, vulgarity, or profanity that debases a target or group online. it is not only limited to detecting offensive language @xcite , cyberbullying @xcite , and hate speech @xcite , but also to more nebulous or implicit forms of abuse. many social media companies and researchers have utilised multiple resources, including machine learning, human reviewers and lexicon-based text analytics to detect abusive language @xcite . however, none of them can perfectly resolve the ald task because of the difficulties of moderating user content and in classifying ambiguous posts @xcite . on the technical side, previous ald models were developed on only a few subtasks (e.g. hate speech, racism, sexism) in a single domain (like twitter), and each specialised model is not successfully transferable to general ald in different online communities. our research question is, "what would be the best generic ald model that can be used for different types of abusive language detection sub-tasks and in different online communities?" to solve this, we found that @xcite reviewed the existing online abusive language detection literature, and defined a generic abusive language typology that can encompass the targets of a wide range of abusive language subtasks in different types of domain. the typology is categorised in the following two aspects: 1) target aspect: the abuse can be directed towards either a) a specific individual/entity or b) a generalised group. this is an essential sociological distinction as the latter refers to a whole category of people, like a race or gender, rather than a specific individual or organisation; 2) content aspect: the abusive content can be explicit or implicit. whether directed or generalised, explicit abuse is unambiguous in its potential to be damaging, while implicit abusive language does not immediately dataset source size composition waseem @xcite twitter 16.2k racism(11.97%), sexism(19.43%), none(68.60%) hateval @xcite twitter 13k hateful(42.08%), non-hateful(57.92%) offeval @xcite twitter 13.2k offensive(33.23%), not-offensive(66.77%) davids @xcite twitter 24.8k hate(5.77%), offensive(77.43%), neither(16.80%) founta @xcite twitter 99k abusive(27.15%), hateful(4.97%), normal(53.85%), spam(4.97%) fnuc @xcite fox news discussion imply abuse (through the use of sarcasm, for example). for example, assume that we have a tweet "f***". "you are sooo sweet like other girls". it includes all those aspects; the directed target ("yourself"), the generalised target ("girls"), the explicit content ("f***"), and the implicit content ("you are sooo sweet"). inspired by this abusive language typology, we propose a new generic ald framework, macas (multi-aspect cross attention super joint for ald), using aspect models and a cross-attention aspect gate flow. first, we build four different types of abusive language aspect embeddings, including directed target, generalised target, explicit content, and implicit content. we also propose to use a heterogeneous graph to analyse the linguistic behaviour of each author and learn word and document embeddings with graph convolutional networks (gcns). not every online community (e.g. news forums) allows user-to-user relationship (e.g. follower-following), so we avoid using user-community relationship information. then, we propose a cross-attention aspect gate flow to obtain the mutual enhancement between the two aspects. the gate flow contains two gates, target gate and content gate, then fuses the outputs of those gates. the target gate draws on the content probability distribution, utilising the semantic information of the whole input sequence along with the target source, while the content gate takes in the target aspect probability distribution as supplementary information for content-based prediction. for evaluation, we test six stateof-the-art ald models across seven datasets focused on different aspects and collected from different domains. our proposed model rivals or exceeds those ald methods on all of the evaluated datasets. the contributions of the paper can be summarised as follows: 1) we perform a rigorous comparison of six state-of-the-art ald models across seven ald benchmark datasets, and find those models do not embrace different types of abusive language aspects in different online communities. 2) we propose a generic new ald algorithm that enables explicit integration of multiple aspects of abusive language, and detection of generic abusive language behaviour in different domains. the proposed model rivals state-of-the-art algorithms on ald benchmark datasets and performs best overall. ## the macas ald model we propose the multi-aspect cross attention super joint model for ald. it is designed as an generic ald that can embrace different types of abusive language aspects in different online communities. as shown in figure 1 , macas can be divided into three main phases: 1) multi-aspect features embedding[sec.3.1]. the multi-aspect embedding layer represents understanding of multi-aspects of abusive language for detecting generic abusive language behaviours. we focus on two main aspects, target and content, and each aspect has two sub-aspects. 1) target aspect represents abuse directed towards either a) a specific individual/entity or b) a generalised group (e.g. gender or race). 2) content aspect covers a) explicit or b)implicit. explicit abuse is unambiguous in its potential to be damaging, while implicit abusive language does not immediately impact (e.g. sarcasm). in addition to this, if the platform provides users' historical posts, we apply graph convolutional network(gcn)s to build a word-document graph embedding that represents linguistic behaviours of users. not every online community (e.g. news forums) has user-to-user relationships (e.g. follower-following), so we avoid using user-community relationship and community network information. 2) cross-attention gate flow for integrating multi-aspects [sec.3.2] the cross-attention gate produces the joint integration of the target aspect and content aspect model and obtains the mutual enhancement between the two aspects. this is for producing well-integrated multi-aspects and improving the performance of generic ald. 3) final aggregation of learned ald embeddings [sec.3.3] we aggregate multi-aspect embeddings and the user's linguistic behaviour embedding across the online post using convolutional neural networks, and produce the ald using multi-layer-perceptron. ## evaluation setting we conducted experiments on all seven datasets with and without gcn as well as using the three different types of cross-transformer variances, which will be discussed in 5.2. the gcn embedding dimension for this linguistic behaviour graph is d @xmath0. for transformer encoder configuration, we used dropout @xmath1.5, encoder @xmath2, head @xmath3, and hidden @xmath4. the models are trained with batch @xmath5, and lr(learning rate) and number of epochs differ: waseem: @xmath6, @xmath7, hateval: @xmath8, @xmath9, offeval: @xmath10, @xmath11, davids: @xmath12, @xmath13, founta: @xmath14, @xmath15, fnuc: @xmath16, @xmath17, stormw: @xmath18, @xmath19. the hyper-parameters are decided by splitting the training set into 90:10 training and validation set. the followings are the models evaluated in our experiments. 5 experiments and results ## conclusion abusive language detection is an essential but challenging task, and it is almost impossible to successfully encompass all different abusive language tasks in different domains. the evaluation also shows that most of the state-of-the-art ald algorithms do not generalise their model to different types of abusive language problems or datasets. in this paper, we proposed a new generic abusive language model, called macas, which applied multi-aspect embeddings to represent generalised characteristics of the domain and introduced a cross-attention gate flow model to achieve better performance by mutual enhancement between the target aspect and the content aspect. the results indicate that our framework was successful and effective in capturing abusive language aspects in different domains. compared to other ald models, our model successfully works in general abusive language detection, and it is hoped that macas provides some insight into the future direction of generic abusive language detection.