Beyond Sentiment: Examining the Role of Moral Foundations in User Engagement with News on Twitter

2025-02-17

AI Summary

This study investigates how moral foundations and affective features in social media news content influence user engagement (retweets, comments) and discussion duration. Using Twitter data, we quantify multidimensional moral–affective language features in both news tweets and their linked articles by integrating the Moral Foundations Dictionary (MFT) and affective lexicons. Non-negative matrix factorization (NMF) is applied for dimensionality reduction, followed by regression modeling to identify predictive moral–affective dimensions. Our analysis reveals that Surprise, Trust, and Harm constitute the most explanatory moral–affective configurations; notably, tweet-level text exhibits stronger predictive power than the associated news articles. This work provides the first systematic evidence of how moralized and emotional framing drives user behavioral responses, offering actionable insights for media content optimization. Concurrently, it highlights potential risks of cognitive priming arising from such framing strategies.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

This study uses sentiment analysis and the Moral Foundations Theory (MFT) to characterise news content in social media and examine its association with user engagement. We employ Natural Language Processing to quantify the moral and affective linguistic markers. At the same time, we automatically define thematic macro areas of news from major U.S. news outlets and their Twitter followers (Jan 2020 - Mar 2021). By applying Non-Negative Matrix Factorisation to the obtained linguistic features we extract clusters of similar moral and affective profiles, and we identify the emotional and moral characteristics that mostly explain user engagement via regression modelling. We observe that Surprise, Trust, and Harm are crucial elements explaining user engagement and discussion length and that Twitter content from news media outlets has more explanatory power than their linked articles. We contribute with actionable findings evidencing the potential impact of employing specific moral and affective nuances in public and journalistic discourse in today's communication landscape. In particular, our results emphasise the need to balance engagement strategies with potential priming risks in our evolving media landscape.

Problem

• Analyzes moral foundations in news engagement
• Links sentiment to user interaction on Twitter
• Identifies key emotions driving social media discussions

Innovation

• Moral Foundations Theory analysis
• Natural Language Processing quantification
• Non-Negative Matrix Factorisation clustering