Target-Based Offensive Language Identification (2023)

Abstract: We present TBO, a new dataset for Target-based Offensive language identification. TBO contains post-level annotations regarding the harmfulness of an offensive post and token-level annotations comprising of the target and the offensive argument expression. Popular offensive language identification datasets for social media focus on annotation taxonomies only at the post level and more recently, some datasets have been released that feature only token-level annotations. TBO is an important resource that bridges the gap between post-level and token-level annotation datasets by introducing a single comprehensive unified annotation taxonomy. We use the TBO taxonomy to annotate post-level and token-level offensive language on English Twitter posts. We release an initial dataset of over 4,500 instances collected from Twitter and we carry out multiple experiments to compare the performance of different models trained and tested on TBO.

## introduction confrontational and often offensive behavior is pervasive in social media. online communities, social media platforms, and tech companies are well aware of the problem and have been investigating ways to cope with the spread of offensive language. this has sparked growing interest in the ai and nlp communities in identifying offensive language, aggression, and hate speech in user-generated content @xcite @xcite . the interest in this topic has motivated the study of offensive language online from different angles. popular shared tasks organized in the past few years have created benchmark datasets, e.g., olid @xcite , which are widely used in research on this topic. ## methods we experimented with three types of models: triple prediction models since the goal of tbo is to predict all elements of an offensive tweet (target, argument, and harmfulness), we are more interested in models that can output triples instead of individual elements. therefore, we used the following models capable of predicting triples. sequence labeling @xcite where a bilstm is used to extract targets and arguments separately and then we train a relation prediction model to predict the harmfulness. dependency graph, adapted from the head-final approach of @xcite , where the target, the arguments, and the harmfulness are modeled as a dependency graph parsing problem. finally, two versions in racl (chen and qian, 2020): racl-glove and racl-bert, which use glove 840b and bert-large as input embeddings, respectively. ## results ## conclusion and future work we presented our novel target-based offensive language identification (tbo) taxonomy, which we used to annotate a new english dataset with over 4,500 tweets. we further evaluated the performance of various models on this new dataset and we discussed the evaluation results in detail. we release all data as well as our code publicly. we believe that the tbo taxonomy and our dataset will be widely used in research on this topic as they have addressed important gaps in previous annotation taxonomies, most notably target identification. in future work, we plan to annotate more data using the taxonomy proposed above, including other languages. this will allow us to take advantage of existing cross-lingual learning models for making predictions as well as for studying cross-language and cross-cultural aspects of offensive language online. we would also like to create comparable annotated tbo datasets for other languages, which will allow us to take advantage of existing cross-lingual models for offensive language identification @xcite . we believe that this will get us closer to what online platforms need @xcite .