Research on the Impact of Algorithmic Echo Chambers on Perceptions and Attitudes of Social Network Users in a Digital Society
Miroslav Gombár, Marija Boban
Abstract
Algorithmic echo chambers on social media significantly influence user perceptions and societal polarization by reinforcing emotionally charged and ideologically aligned content. This study employs secondary data analysis and critical discourse analysis to examine how algorithmic curation affects attitudes toward social values and political beliefs. Findings show that emotionally intense narratives achieve engagement rates of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$85-90 {\%}$</tex>, compared to 30-40% for neutral content, amplifying ideological silos and reducing informational diversity. Case studies from platforms like Facebook, TikTok, and Instagram illustrate how hybrid threats exploit algorithmic amplification to destabilize societal cohesion. The analysis highlights structural biases in algorithmic prioritization mechanisms. It underscores the urgent need for transparent governance, independent oversight, and diversified content strategies to mitigate the societal risks of personalized content distribution.