On the Challenges of Detecting Rude Conversational Behaviour

TL;DR

This paper explores the challenges of detecting rude behavior in conversations using machine learning, categorizing rudeness, analyzing acoustic and semantic signals, and discussing inherent difficulties and future directions.

Contribution

It introduces a categorization of rudeness in conversations and evaluates machine learning methods for detection based on acoustic and semantic features.

Findings

Identification of three distinct rudeness categories

Machine learning can partially detect rudeness from conversation signals

Highlighting key challenges and future research directions

Abstract

In this study, we aim to identify moments of rudeness between two individuals. In particular, we segment all occurrences of rudeness in conversations into three broad, distinct categories and try to identify each. We show how machine learning algorithms can be used to identify rudeness based on acoustic and semantic signals extracted from conversations. Furthermore, we make note of our shortcomings in this task and highlight what makes this problem inherently difficult. Finally, we provide next steps which are needed to ensure further success in identifying rudeness in conversations.