Representations of Emotions in Algorithmic Digital News: A Semiological Approach to Analyzing Artificial Emotional Intelligence

Document Type : Original Article

Author

Journalism Department,, Faculty of Mass Communication, Cairo University

10.21608/jsb.2025.418067.1968

Abstract

The study concluded that algorithms are no longer merely technical tools for content filtering; rather, they have evolved into semantic agents that reconstruct news and guide audience responses through dominant emotional frameworks. The findings revealed that emotion constitutes a core structure in shaping headlines, texts, and images, with the analyzed platforms employing both visual and linguistic signs to reinforce feelings of fear, hope, or pride. Furthermore, affective and suggestive language prevailed over factual reporting through the frequent use of terms such as “tragedy,” “shocking,” and “heroic,” which direct readers toward specific emotional responses. The study also found a moderate alignment between images and emotions (75%–82%), while notable gaps emerged in platforms that relied on archival rather than context-specific visuals. Overall, the results indicate that algorithms reproduce collective affect through mechanisms of repetition and text–image integration, in ways that reflect affective biases shaped by the cultural and political contexts of the platforms. Accordingly, the study recommends integrating semiological analysis into digital journalism practices to enhance credibility and mitigate the influence of artificially engineered emotions.

Keywords

Main Subjects

Journal of Mass Communication Research
Volume 76, Issue 2
October 2025
Pages 833-900

Files

Share

How to cite

Statistics