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New neural network models social media virality dynamics

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel fractional physics-informed neural network designed to model the dynamics of social media virality. This model learns both temporal memory and nonlinear growth patterns, translating them into interpretable equations through symbolic regression. Initial experiments on synthetic data showed promise, but noise in real-world observations required a fully whitened residual to accurately estimate parameters. When applied to 348 posts from X (formerly Twitter), a segmented approach using four 24-hour periods significantly improved performance, revealing segment-specific dynamics and growth laws. AI

IMPACT This research offers a new method for understanding and predicting engagement dynamics on social media platforms.

RANK_REASON The item is an academic paper detailing a new model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New neural network models social media virality dynamics

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The item is an academic paper detailing a new model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alex Koong, George Em Karniadakis ·

    Fractional physics-informed neural networks to model social media virality

    arXiv:2611.07215v1 Announce Type: new Abstract: Existing models of social media virality either prescribe the functional form of engagement growth or assume a fixed mechanism by which past activity influences future engagement. We present a fractional physics-informed neural netw…

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