Algorithmic Misinformation and Editorial Vulnerability: A Statistical Study on the Impact

Abstract

Algorithmic misinformation and AI powered fake news have created unprecedented pressure on editorial control for emerging journalists working in digital media environments. This study statistically investigates the impact of fake news exposure social media dependency and newsroom time pressure on editorial decision making integrity fact checking habits and professional credibility among early career journalists in Delhi NCR within the context of algorithmic news manipulation. A cross sectional survey design was used to collect data from 350 emerging journalists including journalism interns newsroom trainees digital content writers and young journalists working in newsrooms from accredited media institutions and digital newsrooms across Delhi NCR through stratified random sampling. A structured questionnaire based on a five point Likert scale was administered and reliability was established using Cronbachs Alpha α equals 0.87. Data were analyzed using SPSS version 27 through descriptive statistics Pearson correlation multiple linear regression and one way ANOVA. The results showed a strong positive correlation between editorial vulnerability and exposure to algorithmic fake news r equals 0.612 p less than 0.001. Multiple regression analysis demonstrated that the three variables explained 54.3 percent of the variance in editorial vulnerability R2 equals 0.543. A one way ANOVA further indicated a significant difference in editorial vulnerability across platform types with F equals 11.47 and p less than 0.001. The findings are interpreted in the context of Gatekeeping Theory Agenda Setting Theory and Media Dependency Theory. The study recommends structural reforms in digital newsroom ethics training integration of algorithmic literacy into journalism curricula and the adoption of institutional fact checking policy frameworks for emerging media professionals.