用状态空间模型提前15-30分钟预测虚假信息传播，提升早期干预能力。
Before It's Too Late: A State Space Model for the Early Prediction of Misinformation and Disinformation Engagement
- 基于区间删失建模与时间嵌入，捕捉传播初期的细粒度动态
- 首30分钟预测RMSE低至0.118-0.143，比顶尖模型提升4.72%
- 支持长达28天的传播趋势预测，适合平台内容审核与危机响应
在数字时代，阴谋论与信息战可迅速滋生并破坏社会凝聚力。尽管现有深度学习方法在语言与传播建模上取得进展，但对不规则采样数据和早期轨迹评估仍存在挑战。我们提出IC-Mamba，一种新型状态空间模型，通过整合时间嵌入对区间删失数据进行社交互动预测。该模型在发布后关键的15-30分钟内表现优异（RMSE 0.118-0.143），实现内容传播范围的快速评估。通过将区间删失建模引入状态空间框架，IC-Mamba有效捕捉了互动增长的细微时间动态，在点赞、分享、评论及表情符号等多指标上较现有最优方法提升4.72%。实验表明，其在帖子级动态与更广泛叙事模式预测中均具有效性（叙事级F1 0.508-0.751）。模型在长周期预测中表现稳健，仅用3-10天观测窗口即可预测未来28天的意见互动。这些能力使问题内容能被更早识别，为应对策略提供关键缓冲期。代码公开于：https://github.com/ltian678/ic-mamba。交互式演示仪表板见：https://ic-mamba.behavioral-ds.science。
原文摘要 · Abstract (English)
In today's digital age, conspiracies and information campaigns can emerge rapidly and erode social and democratic cohesion. While recent deep learning approaches have made progress in modeling engagement through language and propagation models, they struggle with irregularly sampled data and early trajectory assessment. We present IC-Mamba, a novel state space model that forecasts social media engagement by modeling interval-censored data with integrated temporal embeddings. Our model excels at predicting engagement patterns within the crucial first 15-30 minutes of posting (RMSE 0.118-0.143), enabling rapid assessment of content reach. By incorporating interval-censored modeling into the state space framework, IC-Mamba captures fine-grained temporal dynamics of engagement growth, achieving a 4.72% improvement over state-of-the-art across multiple engagement metrics (likes, shares, comments, and emojis). Our experiments demonstrate IC-Mamba's effectiveness in forecasting both post-level dynamics and broader narrative patterns (F1 0.508-0.751 for narrative-level predictions). The model maintains strong predictive performance across extended time horizons, successfully forecasting opinion-level engagement up to 28 days ahead using observation windows of 3-10 days. These capabilities enable earlier identification of potentially problematic content, providing crucial lead time for designing and implementing countermeasures. Code is available at: https://github.com/ltian678/ic-mamba. An interactive dashboard demonstrating our results is available at: https://ic-mamba.behavioral-ds.science.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。