Timely Decision Analysis Enabled by Efficient Social Media Modeling
Timely Decision Analysis Enabled by Efficient Social Media Modeling
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DOI：
10.1287/deca.2017.0360
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Theodore T. Allen;Zhenhuan Sui;Nathan L. Parker
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文献类型：
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作者：
Theodore T. Allen;Zhenhuan Sui;Nathan L. Parker
许多决策问题都是在不断变化的环境中产生的。例如，确定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”，该漏洞导致事件发生率特别高。这就需要及时的信息来更新决策模型，以便为每个决策周期生成最优策略。社交媒体提供了相关信息的流媒体来源，但这些信息需要有效地转换为数字才能实现所需的更新。本文探讨了如何使用社交媒体作为及时决策的观察来源。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 均值潜在狄利克雷分配 (KLDA)。我们使用网络安全问题来说明该方法。许多组织忽视了在围产期发现的“中等”漏洞...
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...