Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse (2021)

Abstract: Online abuse and offensive language on social media have become widespread problems in today’s digital age. In this paper, we contribute a Reddit-based dataset, consisting of 68,159 insults and 51,102 compliments targeted at individuals instead of targeting a particular community or race. Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset. Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.

## introduction online abuse and targeted negative comments on online social networks have become a prevalent phenomenon, especially impacting adolescents. victims of prolonged and targeted online harassment can experience negative emotions leading to adverse consequences such as anxiety and isolation from the community, which can, in turn, lead to suicidal behaviour. 1 various attempts have been made to detect such harassment @xcite and hate speech @xcite in the past, but very few have attempted to transfer the negative aspect of such speech. recently, many new tasks have been introduced in the domain of text style transfer. however, since parallel corpora is usually not available, most style transfer approaches adopt an unsupervised manner @xcite @xcite . we contribute a dataset of non-parallel sentences, each sentence being either an insult or a compliment, collected from reddit, more specifically, from three subreddits r/roastme, 1 source: https://www.stopbullying.gov/resources/facts figure 1 : examples of indirect insults and compliments with attributes highlighted in bold r/toastme, and r/freecompliments. some examples of such sentences can be seen in figure 1 . with a diverse range of online communication platforms being introduced across the world, and existing platforms' user-bases growing at a fast pace, moderation of such negative comments and harassment becomes even more necessary. we hope that our work can enable and further research in this daunting task, for instance in building moderation systems which can detect such negative speech and nudge users to engage in more positive and non-toxic discourse. reddit is a popular social media website with forums known as subreddits where users can comment and vote on posts and other comments. it has been used as a source of data in wide variety of tasks. r/roastme can be described as a sub-reddit consisting of "abrasive humor" and consists of "creative" insults where users can voluntarily submit their picture to be "roasted." r/toastme and r/freecompliments are similar in principle but have the opposite purpose. a more detailed description of the data source and preparation can be found in section 3. since "creativity" is encouraged in r/roastme, this makes our dataset consist of indirect insults that do not necessarily use any profanity or curse words and may slip past most existing toxic speech filters. in this work, we release the jdc (jibe and delight corpus), a dataset of ∼ 120, 000 reddit comments tagged as insults or compliments which are targeted towards particular attributes of an individual including their face, hair, and eyes foot_0 . we also propose to use classification models to detect and style transfer models to convert such targeted negative comments, often associated with online harassment, in which menacing or insulting messages are sent by direct messages or posted on social media. we also perform benchmarking experiments using existing state-of-the-art models on both fronts and analyse its results. ## related work existing work primarily focuses on the detection of offensive language or hate speech on social media using classification models @xcite @xcite , and not on transferring the negative aspect of such speech into a positive counterpart. detection usually involves either lexical or rule-based approach @xcite , or more recently, a supervised learning approach @xcite . many attempts on detection of specific types of toxic speech have also been attempted @xcite . previous work on text style transfer has largely focused on transferring attributes of sentiment in reviews @xcite @xcite or converting factual captions to humorous or romantic ones @xcite . other tasks include transferring formality @xcite or gender or political style @xcite . recently, transferring politeness has also been proposed by @xcite . most approaches use unsupervised methods since parallel data is usually not available. these approaches can be broadly divided into three groups: 1) explicit disentanglement @xcite which separates content from style attributes in an explicit manner and then combines the separated content with the target attribute and pass it through a generator. 2) disentanglement in latent space @xcite which tries to separate style from content within the embedding space by using suitable objective functions. 3) adversarial or reinforcement learning based @xcite approaches in which disentanglement may not be even required. reddit has been widely used in multiple natural language processing tasks as a data source. while @xcite use reddit to create a large corpus for sarcasm, nogueira dos santos et al. (2018) source their data from r/politics on reddit along with twitter. many controversial subreddits such r/the donald have been used for detection of hate speech in the past @xcite . although nogueira dos santos et al. ( 2018 ) proposed the task of translating offensive sentences to non-offensive ones using style transfer, in our work, we go one step further and propose to convert offensive sentences into positive compliments. prior work on r/roastme has mostly been on a sociopragmatic perspective @xcite . however, there is no previous work that uses r/roastme as a data source in a style transfer task to the best of our knowledge. ## the jdc dataset we contribute the jibe and delight corpus (jdc), a new non-parallel style transfer dataset consisting of ∼ 120, 000 comments tagged as insults or compliments, and perform experiments and analysis on the same. ## experiments & results we perform experiments using both classification and style transfer models and evaluate the performance of five models for each task from works on the jdc. we also discuss about the challenges faced and the metrics used for evaluation. ## conclusion in this work, we introduced a reddit-based dataset consisting of indirect insults that favor creativity and rarely use slurs. we benchmarked classification models for detection and exploited unsupervised text style transfer to convert insults into compliments. we evaluated the performance of different state-of-the-art models on the dataset, observing that while detection is easier, transfer of the negative attribute is a challenging task. future work may include enhancing methodologies for unsupervised text style transfer that capture the intricacies in the proposed dataset and building moderation systems for online platforms.