Abstract

In recent years, an increasing number of researchers have started to explore social media as a medium for disseminating early-stage research ideas, offering faster reach, broader audiences, and richer engagement than traditional journal publishing. However, AI-driven algorithms on social media platforms often result in echo chambers, which distort feedback and reinforce pre-existing biases. This study proposes the Cross-Platform Feedback Method, a systematic approach to mitigate echo chamber effects. With the assistance of AI, the proposed method distributes adapted versions of the same research idea across multiple social media platforms with varying audience demographics, community norms, and algorithmic structures. The proposed method is a practical tool for systematically tailoring and comparing idea dissemination across different social media environments. It also contributes to the broader discourse on scholarly practices in the AI era by demonstrating how researchers can harness AI-driven algorithmic and cultural differences across platforms, while also addressing the psychological challenges of public engagement.

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