This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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?" 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).
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