How Bias Amplifies Across AI Generations | HackerNoon
Briefly

The article discusses a comprehensive framework for analyzing media bias rooted in Entman's framing theory. It employs qualitative methods to explore how biases manifest in text-only formats, emphasizing the significance of story framing and selection bias. Given that visual cues are absent, the framework offers insights on how narrative construction can highlight certain aspects of reality while obscuring others, thus shaping audience perceptions. This analysis aims to enhance understanding of political biases in media texts and their impact on public interpretation of issues.
Qualitative methods confirmed findings in media bias, utilizing a framework grounded in Entman's framing theory. It focuses on political biases in media texts, especially story framing and selection bias.
The framework highlights that certain media biases, like visual or tone biases seen in visual formats, do not apply to our exclusive text data.
Political biases in media arise when topics or narratives are selected or framed in ways that favor certain interpretations over others.
Our analysis reveals how the selection and framing of stories can significantly shape audience perceptions, highlighting the critical role of text in bias evaluation.
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