Quantifying moral foundations from various topics on Twitter conversations

Abstract

Moral foundations theory explains variations in moral behavior using innate moral foundations: Care, Fairness, Ingroup, Authority, and Purity, along with experimental supports. However, little is known about the roles of and relationships between those foundations in everyday moral situations. To address these, we quantify moral foundations from a large amount of online conversations (tweets) about moral topics on the social media site Twitter. We measure moral loadings using latent semantic analysis of tweets related to topics on abortion, homosexuality, immigration, religion, and immorality in general, showing how the five moral foundations function in spontaneous conversations about moral violating situations. The results indicate that although the five foundations are mutually related, Purity is the most distinctive foundation and Care is the most dominant foundation in everyday conversations on immorality. Our study shows a new possibility of natural language processing and social big data for moral psychology.

16 pages, 5 figures, 4 tables, The Proceedings of the 2016 IEEE International Conference on Big Data

References in corpus (1)

• Concurrent Bursty Behavior of Social Sensors in Sporting Events

Cited by in corpus (5)

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• Development and Validation of the Japanese Moral Foundations Dictionary
• Morality-based Assertion and Homophily on Social Media: A Cultural Comparison between English and Japanese Languages
• Inference of Personal Attributes from Tweets Using Machine Learning
• Quantifying Latent Moral Foundations in Twitter Narratives: The Case of the Syrian White Helmets Misinformation