Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships
David Jurgens, Agrima Seth, Jackson Sargent, Athena Aghighi, Michael, Geraci

TL;DR
This paper presents a novel approach that models social relationships to improve the detection of inappropriate messages, emphasizing the importance of context and norms in interpersonal communication analysis.
Contribution
It introduces a new dataset and demonstrates how large language models can incorporate relationship context to accurately assess message appropriateness.
Findings
Relationship modeling improves appropriateness detection accuracy.
Contextual cues are essential for understanding social norms.
Appropriateness judgments predict politeness and condescension.
Abstract
Understanding interpersonal communication requires, in part, understanding the social context and norms in which a message is said. However, current methods for identifying offensive content in such communication largely operate independent of context, with only a few approaches considering community norms or prior conversation as context. Here, we introduce a new approach to identifying inappropriate communication by explicitly modeling the social relationship between the individuals. We introduce a new dataset of contextually-situated judgments of appropriateness and show that large language models can readily incorporate relationship information to accurately identify appropriateness in a given context. Using data from online conversations and movie dialogues, we provide insight into how the relationships themselves function as implicit norms and quantify the degree to which…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHate Speech and Cyberbullying Detection · Interpreting and Communication in Healthcare · Sentiment Analysis and Opinion Mining
