Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta-information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user-adaptable and problem-specific machine learning-based retrieval system. An interactive multi-level matrix-based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross-checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real-world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.

中文翻译：

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CommAID：通过交互式动态建模进行通信分析的可视化分析

通信包括元信息和内容。目前，此类数据的自动分析通常侧重于通过社交网络分析的网络方面或利用文本挖掘方法的内容。然而，第一类方法没有利用丰富的内容信息，而后者忽略了对话环境和时间演变，如元信息中所见。与强调整体方法重要性的传播研究相反，这两个方面很少同时应用，因此，它们的组合在自动分析系统中尚未得到足够的重视。在这项工作中，我们旨在通过讨论这种路径的困难和设计决策以及贡献 CommAID 来应对这一挑战，沟通分析整体战略的蓝图。它具有集成的可视化分析设计，可通过动态建模、语义模式检索以及基于用户适应性和特定问题的机器学习检索系统来分析通信网络。基于交互式多级矩阵的可视化有助于使用支持交叉检查和减少上下文切换的内联视觉效果对网络和内容进行集中分析。我们通过案例研究和使用真实世界通信语料库与八位执法专家的形成性评估来评估我们的方法。结果表明，我们的解决方案在集成水平和适用性方面超越了现有技术。通过这一贡献，我们的目标是为更全面的沟通分析方法铺平道路。