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"Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The rapid growth of social media has raised significant concerns regarding radicalization, filter bubbles, and content bias. A recent study published on arXiv investigates the potential of Large Language Models (LLMs) to classify the political ideology of online content, including news articles and YouTube videos. The research reveals that LLMs, through in-context learning, significantly outperform traditional zero-shot and supervised methods, providing a more efficient means of ideological classification. Additionally, the study emphasizes the influence of metadata, such as content source and descriptions, on the classification process. This finding is particularly relevant in today's digital landscape, where understanding the ideological context of content is essential for addressing issues of bias and radicalization. By offering a more adaptable approach to ideology classification, this research contributes to the ongoing discourse on the role of technology in shaping public opinion…
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