AUTHOR=Sonni Alem Febri 
  
TITLE=Algorithmic gender representation in digital journalism: a perspective on platform-mediated masculinities in Indonesian media
  
JOURNAL=Frontiers in Human Dynamics
  
VOLUME=Volume 7 - 2025
  
YEAR=2025
  
URL=https://www.frontiersin.org/journals/human-dynamics/articles/10.3389/fhumd.2025.1735924
  
DOI=10.3389/fhumd.2025.1735924
  
ISSN=2673-2726
  
ABSTRACT=Integrating artificial intelligence into journalism has transformed how gender identities are represented and consumed across digital platforms. This perspective examines the emergence of “platform-specific masculinities” in Indonesian digital journalism, drawing from recent empirical evidence showing a 55-percentage-point decline in traditional masculine representations between 2019 and 2024. We propose the “Algorithmic Gender Representation Paradigm” (AGRP) as a theoretical framework emerging from Indonesian contexts for understanding how platform-specific affordances, machine learning algorithms, business models, and cultural contexts interact to influence journalistic content and audience engagement. Analysis of 240 h of television programming and 1,100 digital media items reveals that digital platforms demonstrate 111% higher engagement rates for emotionally expressive content than traditional masculine representations, particularly among audiences aged 18–24. While television maintains predominantly traditional representations (65%), platforms like TikTok show significantly higher proportions of emotional (42%) and creative (45%) expressions. These patterns reflect not only algorithmic affordances but also divergent business models: advertiser-funded platforms optimizing for engagement versus broadcast television navigating regulatory constraints. Drawing on platform studies, feminist technology scholarship, and glocalization theory, we challenge assumptions about algorithmic neutrality and highlight the need for culturally sensitive AI development in journalism. We identify critical gaps in cross-cultural algorithmic bias studies and propose methodological approaches for examining long-term societal impacts. The perspective concludes that understanding algorithmic influence on gender representation requires interdisciplinary collaboration, integrating communication studies, computer science, gender studies, and area studies to ensure that digital transformation serves democratic values and promotes culturally sensitive representation across diverse global contexts.