That is Unacceptable: the Moral Foundations of Canceling

2025-02-17

Annual Meeting of the Association for Computational Linguistics

AI Summary

Existing research on “cancel culture” overlooks moral dimensions and overrelies on political ideology to explain evaluative disagreements. Method: We address this gap by constructing CADE—the first annotated dataset for cancel attitude detection—grounded in Moral Foundations Theory (MFT). CADE features multi-dimensional human annotations and statistical modeling of moral intuitions, event types, agent characteristics, and contextual factors. Contribution/Results: Our analysis provides the first empirical evidence that moral orientation constitutes an independent axis of disagreement, distinct from political stance; annotators’ moral foundations significantly predict their cancel attitudes; and situational factors (e.g., event type, involved agents) explain evaluative variance more robustly than individual traits. We propose an “event-centered” framework, offering a novel benchmark and reproducible data resource for designing fair, context-aware governance mechanisms on social platforms.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Reasoning under Uncertainty: Causality
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Canceling is a morally-driven phenomenon that hinders the development of safe social media platforms and contributes to ideological polarization. To address this issue we present the Canceling Attitudes Detection (CADE) dataset, an annotated corpus of canceling incidents aimed at exploring the factors of disagreements in evaluating people canceling attitudes on social media. Specifically, we study the impact of annotators' morality in their perception of canceling, showing that morality is an independent axis for the explanation of disagreement on this phenomenon. Annotator's judgments heavily depend on the type of controversial events and involved celebrities. This shows the need to develop more event-centric datasets to better understand how harms are perpetrated in social media and to develop more aware technologies for their detection.

Problem

• Detecting canceling attitudes on social media platforms
• Understanding moral foundations behind canceling disagreements
• Analyzing event-centric factors in canceling incidents

Innovation

• CADE dataset for canceling attitudes detection
• Analyzing morality impact on annotator disagreement
• Event-centric datasets for harm understanding