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    Visual Analytics of Conversational Dynamics

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    Date
    2019
    Author
    Seebacher, Daniel ORCID
    Fischer, Maximilian T. ORCID
    Sevastjanova, Rita ORCID
    Keim, Daniel A. ORCID
    El-Assady, Mennatallah
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    Abstract
    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.
    BibTeX
    @inproceedings {10.2312:eurova.20191130,
    booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
    editor = {Landesberger, Tatiana von and Turkay, Cagatay},
    title = {{Visual Analytics of Conversational Dynamics}},
    author = {Seebacher, Daniel and Fischer, Maximilian T. and Sevastjanova, Rita and Keim, Daniel A. and El-Assady, Mennatallah},
    year = {2019},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-087-1},
    DOI = {10.2312/eurova.20191130}
    }
    URI
    https://doi.org/10.2312/eurova.20191130
    https://diglib.eg.org:443/handle/10.2312/eurova20191130
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
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