New LLF framework and datasets tackle social platform forecasting

Researchers have introduced a new framework called Lead-Lag Forecasting (LLF) to address the challenge of predicting future impacts based on early user interactions on social platforms. To support this research, they have created two large benchmark datasets derived from arXiv and GitHub, encompassing millions of papers and repositories respectively. These datasets are designed to capture long-term dynamics and avoid sampling biases, providing a foundation for developing and testing LLF models. AI

IMPACT Establishes a new forecasting paradigm for analyzing long-term user behavior dynamics on social platforms.

RANK_REASON The cluster contains an academic paper introducing a new methodology and benchmark datasets. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• GitHub
• Lead-Lag Forecasting
• Yangfanyu Yang

• paper
• product

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