从社交媒体中的噪声计数中恢复鲁棒的时空信号
Robust Spatio-Temporal Signal Recovery from Noisy Counts in Social Media
摘要：
许多现实世界的现象可以用时空信号来表示：在哪里，什么时候，有多少。对于那些希望监测此类信号的人来说，社交媒体是一个诱人的数据来源。与大多数先前的工作不同，我们假设目标现象是已知的，并且我们给出了一种方法来计算其在社交媒体中的发生率。然而，计数是困扰样本偏差，不完整的数据，矛盾的是，数据稀缺-问题没有充分解决以前的工作。我们制定信号恢复作为一个泊松点过程估计问题。我们明确地将人口偏差，时间延迟和空间失真，以及时空正则化纳入模型中，以解决噪声计数问题。我们提出了一个有效的优化算法，并讨论了它的理论性质。我们表明，我们的模型比常用的基线更准确。最后，我们提出了一个案例研究，野生动物路杀监测，我们的模型产生定性令人信服的结果。
英文摘要：
Many real-world phenomena can be represented by a spatio-temporal signal: where, when, and how much. Social media is a tantalizing data source for those who wish to monitor such signals. Unlike most prior work, we assume that the target phenomenon is known and we are given a method to count its occurrences in social media. However, counting is plagued by sample bias, incomplete data, and, paradoxically, data scarcity -- issues inadequately addressed by prior work. We formulate signal recovery as a Poisson point process estimation problem. We explicitly incorporate human population bias, time delays and spatial distortions, and spatio-temporal regularization into the model to address the noisy count issues. We present an efficient optimization algorithm and discuss its theoretical properties. We show that our model is more accurate than commonly-used baselines. Finally, we present a case study on wildlife roadkill monitoring, where our model produces qualitatively convincing results.
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