Algorithms, Addiction and Self-Esteem: Psychological Resilience in the Meta Era

Abstract

In the Meta Era, every decision and desire is shaped by unseen codes that predict our next move with great precision. Social Media algorithms have become like pervasive all-knowing entities that anticipate, influence, and ultimately dictate our reality. This paper aims to examine the relationship between exposure to recommendation algorithms on social media and psychological resilience, specifically focusing on social media addiction (SMA) and self-esteem. This research employs a quantitative methodology. An online survey was used to collect data from 150 participants aged 18-24, selected via convenience and snowball sampling. It combines the Bergen Social Media Addiction Scale (BSMAS) [Andreassen et al., 2016], Rosenberg Self-Esteem Scale (RSES) [Rosenberg; 1965] and Brief Resilience Scale [BRS; Smith et al., 2008]. Exposure to recommendation algorithms is measured based on self-reported data on the total time spent engaging with recommended content on social media. The above data was statistically analysed using Spearman’s Correlation and Linear regression through SPSS Software. This study aims to contribute to the limited research on the intersection of persuasive technology and psychology. It seeks to highlight the implications of algorithm-driven content and influencer trends on compulsive buying and digital consumerism, using the Fogg Behaviour Model [Fogg, B., 2009]. This study will also explore the significant impacts of widespread disinformation on social media and its role in fuelling societal polarisation. By addressing these issues, this paper ultimately calls for digital interventions and regulation of social media usage, to break the cycle of behavioural addiction.