arXiv：cs.CL· Ansh Gupta, Abhiram Gorle, Aayush Rajesh, Tsachy Weissman·· 6 小时前AI 评分33
基于早期传播树增长预测的社交媒体信息级联与人工介入虚假信息研判
Forecasting the Growth of Social Media Information Cascades: Towards Human-in-the-Loop Misinformation Triage
AI 导读
研究将虚假信息早期研判定义为延续预测问题：仅凭前30分钟活动预测传播树后续增长。在FibVID上，结合节点数与结构深度熵、时间到达熵的模型将log未来增长的R²从0.307提升至0.323，log-MAE降低2.4%；97棵高活跃树的R²从0.248升至0.395，Spearman's ρ从0.394升至0.529。
正文
Abstract:Limited review teams must identify which emerging claims are likely to keep growing before their eventual reach is known. We center early misinformation triage on this continuation-forecasting problem: predicting subsequent recorded propagation-tree growth from the first 30 minutes of activity. On FibVID, we compare early node count with structural depth entropy, temporal arrival entropy, and their pair while keeping all propagation trees from each original claim in one partition. Across 352 test trees from 59 claim groups separate from training, the combined model raises $R^2$ for log-transformed future growth from 0.307 to 0.323 and reduces log-MAE by 2.4% (95% claim-bootstrap CI, -0.4% to 5.2%). The gain is especially pronounced among 97 high-activity trees: $R^2$ rises from 0.248 to 0.395, Spearman's $\rho$ from 0.394 to 0.529, and log-MAE falls by 11.7% (95% CI, -1.9% to 23.9%). Complementing the 30-minute growth forecast, we analyze the first 15 replies in 563 PHEME threads. In this cohort, the 15th reply arrives after a median of 28.8 minutes; 52.0% reach the fixed reply prefix within 30 minutes and 71.6% within one hour. Even without the LLM-generated factual-accuracy dimension, the remaining stance, communicative, and affective state composition retains cross-event ranking signal (ROC-AUC 0.538); including that dimension increases ROC-AUC to 0.562. In a separate Check-COVID evaluation of 229 claims, reciprocal-rank fusion retrieves a gold evidence document within the top five for 74.2% of claims and within the top 20 for 94.3%; sentence reranking reaches Recall@20 of 58.1%. We propose an integrated human-review system that brings these early forecasts, response patterns, and retrieved evidence together for misinformation triage relying on the potential virality of claims.
| Comments: | Work done as part of the SHTEM Internship at Stanford University |
| Subjects: | Social and Information Networks (cs.SI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.07209 [cs.SI] |
| (or arXiv:2610.07209v1 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07209 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Aayush Rajesh [view email]
[v1]
Mon, 5 Oct 2026 18:24:18 UTC (397 KB)
来源：arXiv：cs.CL · arxiv.org