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

TL;DR

This paper investigates how moral and emotional language in news tweets influences user engagement, revealing that certain moral foundations like Surprise, Trust, and Harm significantly drive discussion and interaction.

Contribution

It introduces a novel analysis combining Moral Foundations Theory with NLP to quantify moral and affective signals in social media news content and their impact on engagement.

Findings

Surprise, Trust, and Harm are key drivers of engagement.

Twitter news content has more influence on engagement than linked articles.

Employing moral and affective nuances can shape public discourse.

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.