Algorithmic personalization: a study of knowledge gaps and digital media literacy

Understanding personalized content and its societal implications is critical in the digital media era. This article introduces a novel information-analytical system designed to evaluate the level of knowledge among different social classes regarding personalized content in the digital media ecosystem. Utilizing data from 1213 Czech respondents, we employ fuzzy logic and multidimensional membership functions for an in-depth evaluation of the populace's awareness. It categorizes population knowledge on personalization processes, their preferences, and trust levels and advocates control mechanisms over online content. The research reveals significant insights into demographic disparities in digital media literacy, emphasizing the urgent need for targeted educational programs. This paper presents a pioneering methodological framework and lays the groundwork for future investigations into personalized media services' ethical considerations and socio-political dynamics. Our study contributes to the broader discourse on media literacy, algorithmic understanding, and protecting informational self-determination in the digital age.