A Large-scale Comprehensive Abusiveness Detection Dataset with Multifaceted Labels from R eddit (2021)

Abstract: As users in online communities suffer from severe side effects of abusive language, many researchers attempted to detect abusive texts from social media, presenting several datasets for such detection. However, none of them contain both comprehensive labels and contextual information, which are essential for thoroughly detecting all kinds of abusiveness from texts, since datasets with such fine-grained features demand a significant amount of annotations, leading to much increased complexity. In this paper, we propose a Comprehensive Abusiveness Detection Dataset (CADD), collected from the English Reddit posts, with multifaceted labels and contexts. Our dataset is annotated hierarchically for an efficient annotation through crowdsourcing on a large-scale. We also empirically explore the characteristics of our dataset and provide a detailed analysis for novel insights. The results of our experiments with strong pre-trained natural language understanding models on our dataset show that our dataset gives rise to meaningful performance, assuring its practicality for abusive language detection.

## introduction while enhancing the freedom of expression, online discussion has also brought massive harm inflicted by abusive language. to address this problem, numerous studies have attempted to automatically identify abusive expressions in social media @xcite @xcite @xcite . however, the definition of abusive language varies to those aspects the researchers have considered important @xcite . these aspects include lexical profanity (pedersen, * equal contribution † present address: department of psychology, university of michigan, soohyunr@umich.edu. as an outcome of our annotation scheme, we present a large-scale comprehensive abusiveness detection dataset (cadd 2 ). the cadd contains diverse linguistic information with a broad range of aspects of abusive language, detailed in section 3.3. we perform various analyses to confirm that our annotation scheme leads to a high quality and diverse dataset. we make empirical studies of our dataset to look into its characteristics for insights, and assess the performance of strong pre-trained natural language understanding (nlu) models # instances type target d.c. implicit profanity context waseem and hovy (2016 ) 17k poletto et al. (2017) 2k wiegand et al. (2018) 9k de gibert et al. (2018) 10k kumar et al. (2018) 21k basile et al. (2019) 19k zampieri et al. (2019) 14k caselli et al. (2020) 13k cadd (ours) 24k ## related work given that there is little consensus on the clear definition of abusive language @xcite , each study used a different definition of the abusive language and narrowed down its scope accordingly. for instance, some studies focused on lexicon-based profanity @xcite @xcite , while others focused rather on hate speech, which is understood to attack a specific group by mentioning the typical aspects of its members such as age, gender, or sexual orientation @xcite . other researchers focused on the derogatory language that attacks an individual or a group without such identity aspects @xcite . motivated by these types of abusive language (hate speech, derogatory, and profanity), researchers have also worked on identifying information about targets @xcite or demographic characteristics @xcite @xcite , because these are the key factors to discriminate the types. some studies have emphasized that contexts surrounding abusive language should also be taken into account @xcite , or whether it is an implicit abuse such as sarcasm, rhetorical questions, or satire @xcite @xcite . we note that some of these aspects share closely interrelated features, so that they would be best considered together. with the necessity of detecting a broad range of aspects of abusive language, we first sought to include its diverse types. @xcite and @xcite speculated that abusive language annotation could never be complete or made reliable with a single binary value (e.g., "abusive" or "non-abusive"). it is thus not surprising that several studies introduced corpora with novel typologies of abusive language. ## corpus construction we constructed a new dataset to support the tasks of identifying a broad range of aspects of abusiveness from social media texts. the comprehensive abusiveness detection dataset (cadd) was built with posts extracted from the english reddit dataset foot_3 . for annotation on amazon mechanical turk foot_4 (amt), we asked crowdworkers to provide values for multifaceted labels on each data. although we were aware of the trade-off between relying on experts with assured quality and crowdsourcing with scalable quantity (tekiroglu et al., 2020), we eventually chose to crowdsource because it could magnify productivity and provide perspectives of non-experts, amplifying the representativeness of online users. specifically, given that the purpose of the present study lies more in collecting the largescale data that best represent online users rather than maximizing the reliability and consistency, we concluded that crowdsourcing is more appropriate. we conducted two steps of annotation tasks: first, we asked the workers to determine if the comments contain abusiveness, to reduce costs at the second step by maximizing the number of abusive comments; second, we carried out a very detailed annotation using our hierarchical annotation scheme. figure 1 shows an overview of our annotation process. a more detailed explanation for each step is given below. ## experiment 6.1 experimental setup in this section, we report the performance of natural language understanding models on our dataset (cadd). we trained each model on the cadd training set, optimized it on the cadd validation set, and evaluated it on the cadd test set. we randomly shuffled the training data at the beginning of each training epoch. in order to detect abusiveness, we conducted two tasks, using the two most prominent labels out of the multiple labels. the details of each task are as follows: a. task 1 is binary classification to determine whether a text is abusive or not (l2). b. task 2 is multi-class classification to choose one out of four abusive language types (l1). as for the baselines, we implemented two dictionary-based classifiers, support vector machine (svm) and random forest (rf), and three pre-trained transformer models. we experimented with them on the cadd dataset. we fine-tuned svm with linear kernel and @xmath0, and rf where max depth is set to 100. we employed a bert's vocabulary to train dictionary-based models. we fine-tuned transformer models employing the default settings from the huggingface library @xcite : a. bert @xcite is designed to pretrain bidirectional representations using masked language models. we fine-tuned the bert-basecased model. b. albert @xcite has significantly fewer parameters than a traditional bert by two parameter reduction techniques. we fine-tuned the albert-base-v2 model. ## conclusion in this paper, we presented the comprehensive abusiveness detection dataset, covering a broad range of aspects of abusive language. to this end, we designed a hierarchical annotation scheme to allow crowdworkers to annotate multifaceted labels, achieving reliable annotation results. we used the dataset against two tasks discriminating the binary or multiple labels on strong pre-trained nlu models, achieving comparable performance as a baseline, and assuring the practicality of our dataset.