@inproceedings{KauerJoglekarRedietal., author = {Kauer, Tobias and Joglekar, Sagar and Redi, Miriam and Aiello, Luca Maria and Quercia, Daniele}, title = {Mapping and Visualizing Deep-Learning Urban Beautification}, series = {IEEE Computer Graphics and Applications}, volume = {38}, booktitle = {IEEE Computer Graphics and Applications}, number = {5}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {New York}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {1558-1756}, doi = {10.1109/MCG.2018.053491732}, pages = {70 -- 83}, abstract = {Information visualization has great potential to make sense of the increasing amount of data generated by complex machine-learning algorithms. We design a set of visualizations for a new deep-learning algorithm called FaceLift (goodcitylife.org/facelift). This algorithm is able to generate a beautified version of a given urban image (such as from Google Street View), and our visualizations compare pairs of original and beautified images. With those visualizations, we aim at helping practitioners understand what happened during the algorithmic beautification without requiring them to be machine-learning experts. We evaluate the effectiveness of our visualizations to do just that with a survey among practitioners. From the survey results, we derive general design guidelines on how information visualization makes complex machine-learning algorithms more understandable to a general audience.}, subject = {Visualisierung}, language = {en} } @inproceedings{KauerRidleyDoerketal., author = {Kauer, Tobias and Ridley, Arran and D{\"o}rk, Marian and Bach, Benjamin}, title = {The Public Life of Data}, series = {CHI Conference on Human Factors in Computing Systems (CHI '21)}, volume = {2021}, booktitle = {CHI Conference on Human Factors in Computing Systems (CHI '21)}, publisher = {Association for Computing Machinery}, address = {New York}, doi = {10.1145/3411764.3445720}, pages = {1 -- 12}, abstract = {This research investigates how people engage with data visualizations when commenting on the social platform Reddit. There has been considerable research on collaborative sensemaking with visualizations and the personal relation of people with data. Yet, little is known about how public audiences without specific expertise and shared incentives openly express their thoughts, feelings, and insights in response to data visualizations. Motivated by the extensive social exchange around visualizations in online communities, this research examines characteristics and motivations of people's reactions to posts featuring visualizations. Following a Grounded Theory approach, we study 475 reactions from the /r/dataisbeautiful community, identify ten distinguishable reaction types, and consider their contribution to the discourse. A follow-up survey with 168 Reddit users clarified their intentions to react. Our results help understand the role of personal perspectives on data and inform future interfaces that integrate audience reactions into visualizations to foster a public discourse about data.}, subject = {Visualisierung}, language = {en} } @article{KauerAkbabaDoerketal., author = {Kauer, Tobias and Akbaba, Derya and D{\"o}rk, Marian and Bach, Benjamin}, title = {Discursive Patinas}, series = {IEEE transactions on visualization and computer graphics}, volume = {21}, journal = {IEEE transactions on visualization and computer graphics}, number = {1}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {New York}, issn = {1077-2626}, doi = {10.1109/TVCG.2024.3456334}, pages = {1246 -- 1256}, abstract = {This paper presents discursive patinas, a technique to visualize discussions onto data visualizations, inspired by how people leave traces in the physical world. While data visualizations are widely discussed in online communities and social media, comments tend to be displayed separately from the visualization and we lack ways to relate these discussions back to the content of the visualization, e.g., to situate comments, explain visual patterns, or question assumptions. In our visualization annotation interface, users can designate areas within the visualization. Discursive patinas are made of overlaid visual marks (anchors), attached to textual comments with category labels, likes, and replies. By coloring and styling the anchors, a meta visualization emerges, showing what and where people comment and annotate the visualization. These patinas show regions of heavy discussions, recent commenting activity, and the distribution of questions, suggestions, or personal stories. We ran workshops with 90 students, domain experts, and visualization researchers to study how people use anchors to discuss visualizations and how patinas influence people's understanding of the discussion. Our results show that discursive patinas improve the ability to navigate discussions and guide people to comments that help understand, contextualize, or scrutinize the visualization. We discuss the potential of anchors and patinas to support discursive engagements, including critical readings of visualizations, design feedback, and feminist approaches to data visualization.}, subject = {Annotation}, language = {en} } @article{KauerDoerkBach, author = {Kauer, Tobias and D{\"o}rk, Marian and Bach, Benjamin}, title = {Towards Collective Storytelling}, series = {IEEE Computer Graphics and Applications}, volume = {45}, journal = {IEEE Computer Graphics and Applications}, number = {3}, address = {New York}, organization = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {0272-1716}, doi = {10.1109/MCG.2025.3547944}, pages = {17 -- 31}, abstract = {This work investigates personal perspectives in visualization annotations as devices for collective data-driven storytelling. Inspired by existing efforts in critical cartography, we show how people share personal memories in a visualization of COVID-19 data and how comments by other visualization readers influence the reading and understanding of visualizations. Analyzing interaction logs, reader surveys, visualization annotations, and interviews, we find that reader annotations help other viewers relate to other people's stories and reflect on their own experiences. Further, we found that annotations embedded directly into the visualization can serve as social traces guiding through a visualization and help readers contextualize their own stories. With that, they supersede the attention paid to data encodings and become the main focal point of the visualization.}, subject = {Annotation}, language = {en} }