Tutorial Overview
In the present tutorial we propose a way in which online aggression and hate can be annotated, in order to develop systems which can not only detect such aggression and hate but also provide theoretical background for producing potentially inter-operable and transparent datasets.
Verbal aggression and abuse have become a prevalent feature of digital communication on almost all kinds of social media platforms. Considering that aggression can manifest itself in practically infinite forms, it is difficult even for humans to recognise every form of online aggression, not mentioning automated systems. This problem is further exacerbated by a high level of sparsity in the field of Natural Language Processing in terms of taxonomies being used, consistency and explainability of these taxonomies themselves and lack of an understanding of their interrelatedness. Even a cursory glance at multiple manifestations of online language aggression being annotated across datasets results in at least 5 or 6 different types of phenomena being addressed, such as ‘abuse’, ‘aggression’, ‘offense’, ‘toxicity’, ‘hate speech’. Furthermore, one may annotate online data by relying on non-pragmatic categories, such as ‘misogyny’, ‘Islamophobia’, ‘homophobia’, etc. And here we have not even mentioned the micro-level of annotation, i.e. how various aspects of aggression in a particular utterance can be annotated.
The problemacy of annotation implies that it is challenging to develop automated systems through which aggression and hate can be detected in online data.. Along with the aforementioned issue of annotation, a major obstacle in such work has been that most NLP researchers have remained unaware of the significant body of pragmatic research on (im)politeness and verbal aggression. Instead of relying on (im)politeness theory, many NLP experts have used folk formulations to study online aggression. A major issue with using such folk concepts is that they are often incompatible with each other even when one attempts to annotate different datasets in parallel. That is, without making an interdisciplinary effort - by bringing together NLP and (im)politeness research - it is very difficult to study online aggression in a rigorous and replicable way.
In this introductory tutorial we aim to fill this knowledge gap, by proposing an interdisciplinary approach to the annotation of online aggression. We will first provide an overview of both sociopragmatic and pragmalinguistic research on politeness, impoliteness and aggression research, including both theoretical and empirical research. We will then present a new framework for conducting a large-scale corpus-based analysis of these phenomena. We will finally demonstrate how this framework may be used for building datasets that could prove to be more fruitful in designing more transparent and better-performing systems through which one can detect aggression and hate in online settings. The tutorial is expected to be of specific interest for researchers and students working in the burgeoning fields of abusive and offensive language identification and other socially-aware systems.