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Auflistung nach Autor:in "Chounta, Irene-Angelica"

1 - 2 von 2
  • Konferenzbeitrag
    Sentiment Analysis of Participants Interactions in a Hackathon Context: The Example of a Slack Corpus
    (Mensch und Computer 2022 - Tagungsband, 2022) Feislachen, Sarah; Garus, Philip; Wang, Hong; Podkolin, Eduard; Schlüter, Sarah; Schulze Bernd, Nadine; Nolte, Alexander; Manske, Sven; Chounta, Irene-Angelica
    This paper presents the analysis of participants’ interactions during an online hackathon using Natural Language Processing (NLP) techniques. In particular, we explored the communication of groups facilitated by Slack focusing on the use of emojis. Our findings suggest that most used emojis are positive, while negative emojis appeared rarely. Sentiment of written messages was overall positive and could be linked to topics such as motivation or achievements. Topics about participants’ disappointment regarding their progress or the hackathon organization, technical issues and criticism were associated with negative sentiment. We envision that our work offers insights regarding online communication in group and collaborative contexts with an emphasis on group work and interest-based activities.
  • Konferenzbeitrag
    Visual Stability in Dynamic Graph Drawings
    (i-com: Vol. 14, No. 3, 2015) Lezama, Alfredo Ramos; Chounta, Irene-Angelica; Göhnert, Tilman; Hoppe, H. Ulrich
    In graph visualizations, dynamic networks are a special challenge. A typical approach is visualizing the network at several points in time. Drawing these individual time slices often leads to changes in the layout that distract viewers from important information about individual nodes. In this article, we present a mathematical model to quantify the visual stability of dynamic graph drawings. The model takes into account structural and layout-oriented characteristics of the graphs. In order to validate the model, we conducted a study using questionnaires and an eye-tracking device. The participants were asked to track nodes in a dynamic network with three different methods. Then, we compared these methods based on the proposed model, user feedback (questionnaires) and behavioral data (eye-tracking). The results suggest that dynamic graph drawings which assign a fixed position on the canvas to every actor in the network improve the efficiency of the visual search. Nonetheless, more time is required to process the image. In contrast to that, those dynamic graph drawings with a constant shape or with a minimal number of changes require less time to process the image but lose efficiency of visual search.