CRUSH : Contextually Regularized and User anchored Self-supervised Hate speech Detection (2022)

Abstract: The last decade has witnessed a surge in the interaction of people through social networking platforms. While there are several positive aspects of these social platforms, their proliferation has led them to become the breeding ground for cyber-bullying and hate speech. Recent advances in NLP have often been used to mitigate the spread of such hateful content. Since the task of hate speech detection is usually applicable in the context of social networks, we introduce CRUSH, a framework for hate speech detection using User Anchored self-supervision and contextual regularization. Our proposed approach secures ~1-12% improvement in test set metrics over best performing previous approaches on two types of tasks and multiple popular English language social networking datasets.

## introduction today, the world is more connected than ever in the history of mankind. this can primarily be attributed to: (i) the technological advancements that have made affordable internet connections and devices available to people, and (ii) the social networking platforms that have hosted and connected these people. as a result, even people divided by geography can seamlessly interact in real-time without stepping outside their homes. in fact, social networks are an integral part of today's society. we, however, are more concerned about the pitfalls of this global widespread use of social networks. the unprecedented and rapid explosion in social networking and social media use has left many people -particularly the youth, women, and those from ethnic and religious minority groupsvulnerable to the negative aspects of the online world like sexual harassment, fake news and hate figure 1 : [best viewed in color] hateful content tend to cluster together in common threads and usually come from few hateful users in social media. we stress on this informed assumption to learn better representations using self supervision and contextual regularization. in the sub-graph shown in the pic, the textual content is in the form of posts, comments on the posts (optional), and replies to the comments (optional). n u is the total number of users in the network (dynamic), and each of the text sequences can be attributed to one of the users. figure 1 depicts and describes a sub-graph of a typical social network. it is essential to leverage this structure within social networks for infusing network context while identifying hate speech. we investigate and notice that the social graph context can be disentangled into two components: (i) post context: the context in the neighborhood around the text sequence, i.e., the sub-graph consisting of posts, comments, and replies (see figure 1 ) and (ii) user context: the context from all the existing text sequences in the network that originated from the same user (see, for instance, the connections emanating from users 1, 2, n u etc. in the figure 1 ). relying on the echo-chamber effect, we accordingly propose a framework that uses self supervision from unlabelled data harnessed from the social networks (gab & reddit in our case), so that we can use contextual information @xcite to generate better representations from input sentences for hate speech related tasks. the main contributions of this paper are: to the best of our knowledge, we are the first to use text sequence based self-supervision in hate speech using user characteristics. one of the key technical contribution that this paper makes is to show that it is more advantageous to use context to regularize a classification model than directly infusing the context into the model. also, none of our proposed approaches require any additional annotation effort or introduce any extra parameter into the training pipeline, and are therefore scalable. ## related work hate speech is heavily reliant on linguistic complexity. @xcite showed that classification consensus is rare for certain text sequences even among human annotators. automatic detection of hate speech is further strongly tied to the developments in machine learning based methods. until recently, feature engineering was one of the popularly used techniques. gitari et al. (2015) designed several sentiment features and del vigna et al. ( 2017 ) used the sentimental value of words as the main feature to measure the hate constituted within a text sequence. empirical evidence was provided by @xcite indicating that n-gram features and sentiment features can be successfully applied to detect hate speech. @xcite constructed a dataset of hate speech from facebook, and consequently proposed a rich set of sentiment features, including negative sentiment words and negative sentiment symbols, for detecting hateful text sequences. as witnessed by the above works, it was widely believed that sentiment played an important role in detecting underlying hate. more recently, deep learning based methods @xcite have garnered considerable success in detecting hate speech since they can extract the latent semantic features of text and provide the most direct cues for detecting hate speech. @xcite developed a cnn+gru based model to learn higher-level features. @xcite passed pre-trained word embeddings through a fully connected layer, which achieved better performance than contemporary approaches despite its simplicity. tekiroglu et al. (2020) constructed a large-scale dataset for hate speech and its responses and used the pre-trained language models (lm) for detection. these methods demonstrated the considerable advantages of interpreting words in the context of given sentences over static sentiment based methods. self-supervision and auxiliary supervision have also been explored for hate speech detection. hate-bert @xcite used the masked language modelling (mlm) objective to learn con-textual hate semantics within text sequences from individual reddit posts. hatexplain @xcite used human annotators to obtain rationale about the text containing hate speech and then applied the same for improving their language model. researchers have previously tried context infusion in the inputs @xcite @xcite showed that user context helps in downstream tasks. on the other hand, @xcite and @xcite show that contextual classification is harder given contextual annotations. a similar theme recurs when @xcite shows that context infusion does not easily increase the performance of classifiers in context of toxicity detection in text. alternatively instead of using context directly as input we use it as a regularizer to improve the classifier by learning better contextual representations while training. during inference, we use only the post text, thus adding no computation overhead. we find that using self-supervision for hate speech detection systems leveraging the associated context within a social network is heavily underexplored. s demonstrated by @xcite , learning in a broader socio-personal context of the users and contemporary social situations is also very important. the authors showed the clustering tendency of hateful text sequences on specific hateful user timelines and after specific events (temporal clustering). however, to the best of our knowledge, no prior work in hate speech detection has explored self-supervision in these directions using the context information. ## proposed approaches assumptions about invariance(s) are required in all self-supervised learning objectives. we make the following two assumptions articulated below. ## results the further, we can also see that ua and cr phase results individually beat the baseline. furthermore, combined ua+cr phase outperforms all models. hence, the conjunction hypothesis is valid thus answering q3. post (along with ground-truth label) bert hatelm crush if your humor is based on racism homophobia sexism and rape ✗ ✓ ✓ you're not f**king funny go home (non-hate) <@xmath0 discussion of some qualitative examples: that leads us to believe that bert predictions are heavily dependent on the token occurrences rather than the context in the sentence where the tokens have occurred. as for the next two sentences, it is evident that the sentence representations learnt by crush (due to ua + cr phases of training, see section 3.2, 3.3) are superior to those learnt by the hatelm, hence it classifies these text sequences better than simple mlm based training. finally, the last two sentences indicate that crush (along with the other approaches) still lacks the ability to identify humor, sarcasm, and implicit hate speech, which are known to be difficult problems. ## conclusion in this paper, we provide approaches to infuse social network context using the self-supervised userattribution pre-training objective combined with the contextual regularization objective on top of traditional mlm for hate speech tasks. we empirically demonstrate the advantage of our methods by improving over existing competitive baselines in hate speech detection and scoring tasks across three different datasets. we also show that our method performs superior in the low data regime as well when compared to existing approaches. we also do ablations to understand the benefits of each objective separately. future work include exploiting the relations among users, using different base models capable of incorporating longer contexts, and trying to address hard problems like sarcasm and implicit hate speech detection in social networks.