Vol. 7, Issue 2, Part M (2025)

This study examines how social media algorithms influence news consumption patterns and perceptions of bias within the Indian digital context, marked by mobile-first access, high social media penetration, and widespread reliance on messaging apps. As news consumption has shifted from editorially gatekept legacy media to algorithmically curated platforms, concerns surrounding echo chambers, misinformation, and algorithmic bias have intensified. The study investigates the relationship between public perceptions of social media algorithms and their perceived impact on news consumption, echo chambers, and the spread of biased content, testing the hypothesis that algorithms significantly shape news habits and introduce bias. Using a quantitative, cross-sectional design, data were collected through a structured questionnaire from a stratified sample of 406 media professionals and general news consumers in urban India. Four constructs were measured: algorithmic prioritization, echo chambers, personalized consumption, and biased news spread. Bayesian correlation analysis using posterior means and 95% Highest Density Credible Intervals revealed a statistically credible positive relationship between perceived algorithmic prioritization and echo chambers (Posterior Mean=0.151, 95% CI [0.055, 0.244]) and a stronger relationship between personalized consumption and perceived biased news spread (Posterior Mean=0.219, 95% CI [0.127, 0.309]). Other hypothesized relationships were not credible. The findings partially support the hypothesis, indicating that while algorithms shape the structural information environment and bias exposure, individual news choice continues to reflect user agency.