On the Challenges of Detecting Rude Conversational Behaviour
This work addresses the problem of automated rudeness detection in conversations, which is incremental as it builds on existing methods without major breakthroughs.
The study tackled the problem of detecting rude conversational behavior by segmenting rudeness into three categories and using machine learning on acoustic and semantic signals, but it noted shortcomings and inherent difficulties without reporting concrete performance numbers.
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.