@book{AarsethManovichMaeyraeetal.2011,
  author    = {Aarseth, Espen and Manovich, Lev and M{\"a}yr{\"a}, Frans and Salen, Katie and Wolf, Mark J. P.},
  title     = {DIGAREC Keynote-Lectures 2009/10},
  editor    = {G{\"u}nzel, Stephan and Liebe, Michael and Mersch, Dieter},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-115-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-49780},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {159},
  year      = {2011},
  abstract  = {The sixth volume of the DIGAREC Series holds the contributions to the DIGAREC Keynote-Lectures given at the University of Potsdam in the winter semester 2009/10. With contributions by Mark J.P. Wolf (Concordia University Wisconsin), Espen Aarseth (Center for Computer Games Research, IT University of Copenhagen), Katie Salen (Parsons New School of Design, New York), Laura Ermi and Frans M{\"a}yr{\"a} (University of Tampere), and Lev Manovich (University of Southern California, San Diego).},
  language  = {de}
}
@book{BaltzerHradilakPfennigschmidtetal.2021,
  author    = {Baltzer, Wanda and Hradilak, Theresa and Pfennigschmidt, Lara and Prestin, Luc Maurice and Spranger, Moritz and Stadlinger, Simon and Wendt, Leo and Lincke, Jens and Rein, Patrick and Church, Luke and Hirschfeld, Robert},
  title     = {An individual-centered approach to visualize people's opinions and demographic information},
  number    = {136},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-504-0},
  issn      = {1613-5652},
  doi       = {10.25932/publishup-49145},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-491457},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {326},
  year      = {2021},
  abstract  = {The noble way to substantiate decisions that affect many people is to ask these people for their opinions. For governments that run whole countries, this means asking all citizens for their views to consider their situations and needs. Organizations such as Africa's Voices Foundation, who want to facilitate communication between decision-makers and citizens of a country, have difficulty mediating between these groups. To enable understanding, statements need to be summarized and visualized. Accomplishing these goals in a way that does justice to the citizens' voices and situations proves challenging. Standard charts do not help this cause as they fail to create empathy for the people behind their graphical abstractions. Furthermore, these charts do not create trust in the data they are representing as there is no way to see or navigate back to the underlying code and the original data. To fulfill these functions, visualizations would highly benefit from interactions to explore the displayed data, which standard charts often only limitedly provide. To help improve the understanding of people's voices, we developed and categorized 80 ideas for new visualizations, new interactions, and better connections between different charts, which we present in this report. From those ideas, we implemented 10 prototypes and two systems that integrate different visualizations. We show that this integration allows consistent appearance and behavior of visualizations. The visualizations all share the same main concept: representing each individual with a single dot. To realize this idea, we discuss technologies that efficiently allow the rendering of a large number of these dots. With these visualizations, direct interactions with representations of individuals are achievable by clicking on them or by dragging a selection around them. This direct interaction is only possible with a bidirectional connection from the visualization to the data it displays. We discuss different strategies for bidirectional mappings and the trade-offs involved. Having unified behavior across visualizations enhances exploration. For our prototypes, that includes grouping, filtering, highlighting, and coloring of dots. Our prototyping work was enabled by the development environment Lively4. We explain which parts of Lively4 facilitated our prototyping process. Finally, we evaluate our approach to domain problems and our developed visualization concepts. Our work provides inspiration and a starting point for visualization development in this domain. Our visualizations can improve communication between citizens and their government and motivate empathetic decisions. Our approach, combining low-level entities to create visualizations, provides value to an explorative and empathetic workflow. We show that the design space for visualizing this kind of data has a lot of potential and that it is possible to combine qualitative and quantitative approaches to data analysis.},
  language  = {en}
}
@phdthesis{Repke2022,
  author    = {Repke, Tim},
  title     = {Machine-learning-assisted corpus exploration and visualisation},
  doi       = {10.25932/publishup-56263},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-562636},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xii, 131},
  year      = {2022},
  abstract  = {Text collections, such as corpora of books, research articles, news, or business documents are an important resource for knowledge discovery. Exploring large document collections by hand is a cumbersome but necessary task to gain new insights and find relevant information. Our digitised society allows us to utilise algorithms to support the information seeking process, for example with the help of retrieval or recommender systems. However, these systems only provide selective views of the data and require some prior knowledge to issue meaningful queries and asses a system's response. The advancements of machine learning allow us to reduce this gap and better assist the information seeking process. For example, instead of sighting countless business documents by hand, journalists and investigator scan employ natural language processing techniques, such as named entity recognition. Al-though this greatly improves the capabilities of a data exploration platform, the wealth of information is still overwhelming. An overview of the entirety of a dataset in the form of a two-dimensional map-like visualisation may help to circumvent this issue. Such overviews enable novel interaction paradigms for users, which are similar to the exploration of digital geographical maps. In particular, they can provide valuable context by indicating how apiece of information fits into the bigger picture.This thesis proposes algorithms that appropriately pre-process heterogeneous documents and compute the layout for datasets of all kinds. Traditionally, given high-dimensional semantic representations of the data, so-called dimensionality reduction algorithms are usedto compute a layout of the data on a two-dimensional canvas. In this thesis, we focus on text corpora and go beyond only projecting the inherent semantic structure itself. Therefore,we propose three dimensionality reduction approaches that incorporate additional information into the layout process: (1) a multi-objective dimensionality reduction algorithm to jointly visualise semantic information with inherent network information derived from the underlying data; (2) a comparison of initialisation strategies for different dimensionality reduction algorithms to generate a series of layouts for corpora that grow and evolve overtime; (3) and an algorithm that updates existing layouts by incorporating user feedback provided by pointwise drag-and-drop edits. This thesis also contains system prototypes to demonstrate the proposed technologies, including pre-processing and layout of the data and presentation in interactive user interfaces.},
  language  = {en}
}