A Large-Scale E nglish Multi-Label T witter Dataset for Cyberbullying and Online Abuse Detection (2021)

Abstract: In this paper, we introduce a new English Twitter-based dataset for cyberbullying detection and online abuse. Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabilities of various forms of bullying and offensive content, including insult, trolling, profanity, sarcasm, threat, porn and exclusion. We recruited a pool of 17 annotators to perform fine-grained annotation on the dataset with each tweet annotated by three annotators. All our annotators are high school educated and frequent users of social media. Inter-rater agreement for the dataset as measured by Krippendorff’s Alpha is 0.67. Analysis performed on the dataset confirmed common cyberbullying themes reported by other studies and revealed interesting relationships between the classes. The dataset was used to train a number of transformer-based deep learning models returning impressive results.

## introduction cyberbullying has been defined as an aggressive and intentional act repeatedly carried out using electronic means against a victim that cannot easily defend him or herself @xcite . online abuse by contrast can refer to a wide range of behaviours that may be considered offensive by the parties to which it is directed to @xcite . this includes trolling, cyberbullying, sexual exploitation such as grooming and sexting and revenge porn (i.e., the sharing of inappropriate images of former romantic partners). a distinguishing feature of cyberbullying within the wider realm of online abuse is that it is a repeated act and its prevalence on social media (along with other forms of online abuse) has generated significant interest in its automated detection. this has lead to an increase in research efforts utilising supervised machine learning methods to achieve this automated detection. training data plays a significant role in the detection of cyberbullying and online abuse. the domain-bias, composition and taxonomy of a dataset can impact the suitability of models trained on it for abuse detection purposes, and therefore the choice of training data plays a significant role in the performance of these tasks. while profanity and online aggression are often associated with online abuse, the subjective nature of cyberbullying means that accurate detection extends beyond the mere identification of swear words. indeed, some of the most potent abuse witnessed online has been committed without profane or aggressive language. this complexity often requires labelling schemes that are more advanced than the binary annotation schemes used on many existing labelled datasets. this, therefore, influenced our approach in creating the dataset. after extracting data from twitter using targeted queries, we created a taxonomy for various forms of online abuse and bullying (including subtle and indirect forms of bullying) and identified instances of these and other inappropriate content (e.g., pornography and spam) present within the tweets using a finegrained annotation scheme. the result is a large labelled dataset with a majority composition of offensive content. this paper is organised as follows. in section 2, we present an overview of existing online abuserelated datasets. section 3 discusses the collection method, composition, annotation process and usage implications for our dataset. results of the experiments performed using the dataset are discussed in section 4. finally, conclusion and future research are described in section 5. ## related work social media has become the new playground and, much like physical recreation areas, bullies inhabit facets of this virtual world. the continually evolving nature of social media introduces a need for datasets to evolve in tandem to maintain relevance. datasets such as the barcelona media dataset used in studies such as those by @xcite , @xcite , @xcite , nandhini and sheeba (2015) was created over ten years ago and, while representative of social media usage at the time, social networks such as myspace, slashdot, kongregate and formspring from which some of the data was sourced are no longer widely used. the consequence of this is that such datasets are no longer representative of current social media usage. twitter is one of the most widely used social media platforms globally; as such, it is no surprise that it is frequently used to source cyberbullying and online abuse data. bretschneider et al. (2014) @xcite labelled 91,040 tweets for sarcasm. this is noteworthy because while sarcasm can be used to perpetrate online bullying, it is rarely featured in existing cyberbullying datasets' taxonomies. however, as the dataset was created for sarcasm detection only, this is the only context that can be learned from the dataset. as such, any model trained with this dataset will be unable to identify other forms of online abuse, thus limiting its usefulness. @xcite annotated 17,000 tweets using labels like racism and sexism, and @xcite labelled over 25,000 tweets based on the presence of offensive and hate speech. @xcite extracted features to identify cyberbullies by clustering 9,484 tweets attributed to 303 unique twitter users. in creating their bi-lingual dataset sourced from askfm, hee et al. (2018) used a detailed labelling scheme that acknowledges the different types of cyberbullying discovered in the retrieved post types. the dataset's effectiveness in training classifiers may, however, be affected by the low percentage of abusive documents present. this dataset was subsequently re-annotated by @xcite to identify which of the four roles of 'harasser', 'victim', 'bystander defender' and 'bystander assistant' was played by the authors of the posts contained in the dataset. similarly, @xcite used the same four roles to annotate a dataset created from simulated cyberbullying episodes using the instant messaging tool; whatsapp, along with the labels created by hee et al. ( 2018 ) @xcite used a hierarchical annotation scheme that, in addition to identifying offensive tweets, also identifies if such tweets are targeted at specific individuals or groups and what type of target it is (i.e., individual -@username or group -'. . . all you republicans'). hierarchical annotation schemes have indeed shown promise as observed in their use in recent offensive language detection competitions like hateval foot_0 and offen-seval foot_1 ; that said, however, a hierarchical scheme could inadvertently filter out relevant labels depending on the first-level annotation scheme used. @xcite used one of the most comprehensive annotation schemes encountered in an existing dataset and additionally included a very high percentage of positive cyberbullying samples in their dataset but, regrettably, the number of english documents included in the dataset is small in comparison to other datasets. @xcite annotated about 10,000 tweets using labels like abusive, hateful, spam and normal, while bruwaene et al. (2020) experimented with a multi-platform dataset comprising of 14,900 english documents sourced from instagram, twitter, facebook, pinterest, tumblr, youtube and gmail. other notable publicly available datasets include the kaggle insult @xcite and kaggle toxic comments @xcite datasets. a comprehensive review of publicly available datasets created to facilitate the detection of online abuse in different languages is presented in @xcite . ## data in this section, we introduce our dataset and how it addresses some of the limitations of existing datasets used in cyberbullying and online abuse detection research. ## conclusion in this paper, we presented a new cyberbullying dataset and demonstrated the use of transformerbased deep learning models to perform fine-grained detection of online abuse and cyberbullying with very encouraging results. to our knowledge, this is the first attempt to create a cyberbullying dataset with such a high concentration (82%) of cyberbullying and offensive content in this manner and using it to successfully evaluate a model trained with the dataset on a different domain. the dataset is available at https://bitbucket.org/ssalawu/cyberbullyingtwitter for the use of other researchers.