3 research outputs found
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
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.Comment: 12 pages, 7 figures, Computer Graphics Forum 2021 (pre-peer reviewed
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Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers
The automated analysis of digital human communication data often focuses on
specific aspects such as content or network structure in isolation. This can
provide limited perspectives while making cross-methodological analyses,
occurring in domains like investigative journalism, difficult. Communication
research in psychology and the digital humanities instead stresses the
importance of a holistic approach to overcome these limiting factors. In this
work, we conduct an extensive survey on the properties of over forty
semi-automated communication analysis systems and investigate how they cover
concepts described in theoretical communication research. From these
investigations, we derive a design space and contribute a conceptual framework
based on communication research, technical considerations, and the surveyed
approaches. The framework describes the systems' properties, capabilities, and
composition through a wide range of criteria organized in the dimensions (1)
Data, (2) Processing and Models, (3) Visual Interface, and (4) Knowledge
Generation. These criteria enable a formalization of digital communication
analysis through visual analytics, which, we argue, is uniquely suited for this
task by tackling automation complexity while leveraging domain knowledge. With
our framework, we identify shortcomings and research challenges, such as group
communication dynamics, trust and privacy considerations, and holistic
approaches. Simultaneously, our framework supports the evaluation of systems
and promotes the mutual exchange between researchers through a structured
common language, laying the foundations for future research on communication
analysis.Comment: 11 pages, 2 tables, 1 figur
Visual Analytics of Conversational Dynamics
Large-scale interaction networks of human communication are often modeled as complex graph structures, obscuring temporal patterns within individual conversations. To facilitate the understanding of such conversational dynamics, episodes with low or high communication activity as well as breaks in communication need to be detected to enable the identification of temporal interaction patterns. Traditional episode detection approaches are highly dependent on the choice of parameters, such as window-size or binning-resolution. In this paper, we present a novel technique for the identification of relevant episodes in bi-directional interaction sequences from abstract communication networks. We model communication as a continuous density function, allowing for a more robust segmentation into individual episodes and estimation of communication volume. Additionally, we define a tailored feature set to characterize conversational dynamics and enable a user-steered classification of communication behavior. We apply our technique to a real-world corpus of email data from a large European research institution. The results show that our technique allows users to effectively define, identify, and analyze relevant communication episodes.publishe