Revisiting Contextual Toxicity Detection in Conversations

TL;DR

This paper investigates how conversational context influences toxicity detection in conversations, analyzing datasets and proposing neural models and data augmentation techniques to improve detection accuracy, especially in social media contexts.

Contribution

It introduces neural architectures that incorporate conversational structure and data augmentation strategies for enhanced contextual toxicity detection.

Findings

Neural models aware of conversation structure improve toxicity detection.

Synthetic data benefits models in social media toxicity detection.

Human toxicity labeling is influenced by conversational context.

Abstract

Understanding toxicity in user conversations is undoubtedly an important problem. Addressing "covert" or implicit cases of toxicity is particularly hard and requires context. Very few previous studies have analysed the influence of conversational context in human perception or in automated detection models. We dive deeper into both these directions. We start by analysing existing contextual datasets and come to the conclusion that toxicity labelling by humans is in general influenced by the conversational structure, polarity and topic of the context. We then propose to bring these findings into computational detection models by introducing and evaluating (a) neural architectures for contextual toxicity detection that are aware of the conversational structure, and (b) data augmentation strategies that can help model contextual toxicity detection. Our results have shown the encouraging potential of neural architectures that are aware of the conversation structure. We have also demonstrated that such models can benefit from synthetic data, especially in the social media domain.