Toward a Model of Media Literacy and Public Sphere Expansion: A Grounded Theory Approach

The public sphere often struggles with a trade-off between authentic, small-scale discourse and broad, superficial debate dominated by influential entities. This study explores how media and information literacy (MIL) can foster a public sphere that is both expansive and genuinely representative. I investigate this by using Large Language Models (LLMs) to analyze a vast body of scholarly literature and develop a model explaining MIL’s positive impact. Employing a qualitative grounded theory methodology, I used ChatGPT, DeepSeek, and Gemini to perform open, axial, and selective coding on 7,670 abstracts from the Web of Science database published within the last five years. While the LLMs’ coding frameworks differed slightly, they demonstrated significant agreement on abstracts concerning MIL’s role in expanding public discourse. The analysis yielded five to seven core thematic categories and numerous subcategories. Notably, both ChatGPT and Gemini converged on similar overarching frameworks, identified as “civic resilience in the digital age” and “empowered media citizenship.” These concepts align with existing literature linking MIL to human rights, digital citizenship, and sustainable development. The study suggests that a multi-stage grounded theory approach, analyzing smaller literature batches with sophisticated prompt engineering, could yield even deeper insights as LLM technology advances.