Algorithmic Recommendation and Information Cocoons: Analysis of Information Security Issues on Social Media
Abstract
In recent years, social media platforms have increasingly adopted algorithm-driven content delivery mechanisms that personalize user experiences by tailoring recommendations based on interactions such as liking, sharing, commenting, and saving content. This approach, often referred to as the "information cocoon" effect, significantly shapes the digital information landscape by creating highly individualized content streams. The information cocoon algorithm in daily life can enhance users' engagement and cohesion, but at the same time, it limits the exposure of diverse viewpoints, amplifies cognitive biases towards certain fixed opinions, and increases vulnerability to misinformation. This paper critically analyzes the mechanisms through which algorithmic recommendations foster information cocoons and identifies associated risks, including misinformation propagation, social polarization, and algorithmic discrimination. Utilizing a systematic literature review, this study proposes mitigation strategies encompassing enhanced algorithmic transparency, regular independent audits, content diversification, digital literacy enhancement, and regulatory oversight, aiming to safeguard information security in the algorithm-dominated social media landscape.
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