Many decision problems are set in changing environments. For example, determining the optimal investment in cyber maintenance depends on whether there is evidence of an unusual vulnerability, such as “Heartbleed,” that is causing an especially high rate of incidents. This gives rise to the need for timely information to update decision models so that optimal policies can be generated for each decision period. Social media provide a streaming source of relevant information, but that information needs to be efficiently transformed into numbers to enable the needed updates. This article explores the use of social media as an observation source for timely decision making. To efficiently generate the observations for Bayesian updates, we propose a novel computational method to fit an existing clustering model. The proposed method is called k-means latent Dirichlet allocation (KLDA). We illustrate the method using a cybersecurity problem. Many organizations ignore “medium” vulnerabilities identified during peri...

中文翻译：

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通过高效的社交媒体建模实现及时的决策分析

在不断变化的环境中设置了许多决策问题。例如，确定对网络维护的最佳投资取决于是否有证据表明存在异常漏洞，例如“ Heartbleed”，该漏洞导致特别高的事件发生率。这导致需要及时的信息来更新决策模型，以便可以为每个决策周期生成最佳策略。社交媒体提供了相关信息的流媒体源，但是该信息需要有效地转换为数字以启用所需的更新。本文探讨了使用社交媒体作为及时决策的观察源。为了有效地生成贝叶斯更新的观测值，我们提出了一种新颖的计算方法来拟合现有的聚类模型。所提出的方法称为k均值潜在Dirichlet分配（KLDA）。我们将说明使用网络安全问题的方法。许多组织忽略了期间发现的“中等”漏洞。