More moral language doesn’t always mean more engagement online
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Moral language—words and phrases that express ideas about right and wrong, virtues, fairness, harm and social obligations—is widely used in social media posts and other online content. Posts containing moral language sometimes spread quickly and attract a lot of attention on social media, yet whether moral language is always linked to greater user engagement remains unclear.
Researchers at Universidad del Desarrollo in Chile, the University of Massachusetts Amherst and Northwestern University in the U.S. recently analyzed posts and comments from three online platforms to explore the relationship between moral language and online engagement. Their findings, published in a paper in Nature Human Behaviour, suggest that moral language is linked to higher user engagement only up to a point, after which it tends to elicit fewer reposts and replies.
“Earlier research showed that moral language was associated with greater sharing online,” Cristian Candia, first author of the paper, told Phys.org. “We wanted to understand whether that relationship had a limit. A message can express an important moral concern, but does filling it with moral language necessarily make it more engaging?”
Moral language and engagement across three online platforms
Candia and his colleagues wanted to distinguish a message’s overall moral relevance from how saturated it is with moral language. Their goal was to shed light on whether the use of moral language, and the extent to which it is used, are linked to higher or lower engagement.
To conduct their analyses, the researchers combined previously released datasets containing Twitter posts and engagement data with archived public discussions from Reddit and 8chan. Collectively, they analyzed approximately 1.62 million observations comprising Twitter and Reddit posts and 8chan discussions, spanning 13 topics and discussion communities.
“The final analysis included individual posts on Twitter and Reddit and discussions grouped by topic and day on 8chan,” Candia explained.
“Using computational language analysis, we measured how closely messages related to moral concepts and how concentrated that language was. We then examined their relationship with retweets or replies, depending on the platform, accounting for available features such as links and multimedia. We also checked the findings using conventional moral-word dictionaries.”
The team analyzed the data using a computational method called Distributed Dictionary Representations. This approach can be used to determine how closely language relates to concepts included in a predefined dictionary.
The researchers used a pretrained model to represent words as numerical vectors, based on similarities in their meanings and the contexts in which they appeared. They then calculated the moral loading of each post they analyzed (i.e., the degree to which it contained moral ideas) and the moral density (i.e., the concentration of moral content across the words used).
Finally, they tried to determine whether the moral loading and density of posts were associated with user engagement. Their findings revealed that, across all three platforms, the expression of moral ideas (i.e., loading) was consistently linked with greater user engagement.
Greater moral density, however, was associated with lower engagement when the overall moral loading was held constant. When the two relationships were combined, the model estimated that engagement peaked at a calculated moral-density score of approximately 0.30 on average across platforms and topics and declined as the scores increased.
Moral saturation and online engagement. Image showing that more concentrated moral language was associated with lower engagement across Twitter, Reddit and 8chan, holding overall moral relevance constant. Credit: Candia et al., Nature Human Behaviour (2026). The Author(s), under exclusive license to Springer Nature Limited. Model-derived estimates and simulations illustrating how engagement can peak at intermediate levels of moral expression. Note: these simulations are not experimental results. Credit: Candia et al., Nature Human Behaviour (2026). The Author(s), under exclusive license to Springer Nature Limited.
“We found that the relationship between moral language and engagement has limits,” Candia said. “Messages with greater overall moral relevance tended to receive more engagement, but messages with more concentrated moral language received less engagement at comparable levels of moral relevance.
“Our model brings these relationships together to predict an intermediate range of moral expression associated with the highest engagement, which varies across contexts.”
Key insights for behavioral scientists and communicators
The results of the team’s analyses suggest that using more moral language in social media posts is not always linked to more engagement. In fact, posts that are highly saturated with moral language appear to receive less engagement on average.
It should be noted that the study was observational and not experimental. Its findings therefore do not establish whether using more moral language causes differences in engagement.
In the future, behavioral scientists could test this hypothesis experimentally. Meanwhile, the insights gathered by Candia and his colleagues could offer general guidance for communicators and online content creators.
“One question I would like to explore is whether AI-generated messages use moral language differently from human-written messages, and how readers respond to those differences,” Candia added. “More broadly, we still need to understand why moral saturation is associated with lower engagement and whether similar patterns extend to trust, understanding and sustained human attention. I see these as open questions that could guide future work.”
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Publication details
Cristian Candia et al, Saturation of moral language predicts lower content engagement on social media, Nature Human Behaviour (2026). DOI: 10.1038/s41562-026-02560-y.
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More moral language doesn’t always mean more engagement online (2026, September 27)
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