Algorithmically Generated Information Vacuum: Short-Form Video Personalization, Perceived Relevance and Critical Thinking Among Young Media Users

Aleksandra Spasevska

Abstract

This paper explores the idea of an algorithmically generated information vacuum among young users of digital media. Young people today have access to many sources of information. Still, the content they see every day is often shaped by their interests, searches, viewing habits and interactions. Because of this, students may mainly see topics that already attract them. Other current, cultural and socially important topics may remain outside their attention. The paper combines theoretical analysis with a questionnaire-based study among students. Its aim is to examine the relation between information visibility, perceived relevance, attention and critical thinking. The questionnaire focuses on four areas. The first area examines students’ media habits and the type of content they most often see on short-form video platforms, such as TikTok, Instagram Reels and YouTube Shorts. The second area examines students’ awareness of general, cultural and current topics. The third area focuses on the possible influence of short-form content on attention, interpretation and the ability to follow longer and more complex content. The fourth area focuses on critical thinking, especially the ability to distinguish between popular and important content, to check information, and to reflect on the role of algorithms in shaping their everyday information space. The paper argues that critical thinking in digital environments is not only about checking information that appears in front of us. It is also about noticing what remains unseen, unsearched and excluded from one’s everyday digital environment. For this reason, media literacy today should include algorithmic literacy and a stronger focus on the hidden limits of personalized digital content.

Keywords: algorithmic personalization; information vacuum; short-form videos; perceived relevance; critical thinking