Algorithmic Recommendation induced Emotional Burnout among Digital Media Users

Abstract

The digital platforms that are pushed by algorithms have transformed young adult information consumption, socialization, and entertainment. Although AI-enabled recommendation engines provide tailored information to enhance interactivity, there is emerging evidence that longer exposure to these systems is associated with psychological stress levels, such as emotional burnout. The paper analyses AI recommendation-related emotional burnout in young adults living in the Delhi-NCR region of India, investigating the relationships between digital burnout, social media exhaustion, Information overload, and emotional burnout. A quantitative cross-sectional correlational design was used to sample 210 active users (18-30) of such platforms as Instagram, YouTube, and streaming services. Validated scales were the Maslach Burnout Inventory Emotional Exhaustion subscale (MBI-EE), Digital Burnout Scale, Social Media Fatigue Scale and Information Overload Scale. Analysis of data using SPSS was done through descriptive statistics, Pearson correlation analysis and multiple regression. The results reported moderate-to-high prevalence of emotional fatigue. The emotional exhaustion had a positive correlation with digital burnout (r=.536, p<.001), social media fatigue (r=.422, p<.001) and information overload (r=.428, p<.001). There was an association found between social media fatigue and information overload as important and significant predictors of emotional exhaustion and digital burnout, respectively, through regression models. These findings extend the burnout theory to the algorithmic digital ecosystems, and curated feeds are considered chronic stressors. The work proposes the ethical development of AI design, digital literacy, and sustainable digital well-being intervention by shedding light on cognitive-emotional strain-strain pathways in the behavior of young adults.