We consider the problem of modeling and clustering heterogeneous event data arising from coupled conflict event and social media data sets. In this setting conflict events trigger responses on social media, and, at the same time, signals of grievance detected in social media may serve as leading indicators for subsequent conflict events. For this purpose we introduce the Hawkes Binomial Topic Model (HBTM) where marks, Tweets and conflict event descriptions are represented as bags of words following a Binomial distribution. When viewed as a branching process, the daughter event bag of words is generated by randomly turning on/off parent words through independent Bernoulli random variables. We then use expectation–maximization to estimate the model parameters and branching structure of the process. The inferred branching structure is then used for topic cascade detection, short-term forecasting, and investigating the causal dependence of grievance on social media and conflict events in recent elections in Nigeria and Kenya.

中文翻译：

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霍克斯二项式主题模型及其在冲突Twitter数据耦合中的应用

我们考虑建模和聚类由冲突事件和社交媒体数据集耦合产生的异构事件数据的问题。在这种情况下，冲突事件会触发社交媒体上的响应，与此同时，社交媒体中检测到的不满信号可能会成为后续冲突事件的主要指标。为此，我们引入了霍克斯二项式主题模型（HBTM），其中标记，推文和冲突事件描述按照二项式分布表示为单词袋。当视为分支过程时，通过独立的伯努利随机变量通过随机打开/关闭父词来生成子词事件包。然后，我们使用期望最大化来估计过程的模型参数和分支结构。