Visualizing Evaluative Language in Relation to Constructing Identity in English Editorials and Op-Eds

This thesis is concerned with the problem of managing complexity in Systemic Functional Linguistic (SFL) analyses of language, particularly at the discourse semantics level. To deal with this complexity, the thesis develops AppAnn, a suite of linguistic visualization techniques that are specifically designed to provide both synoptic and dynamic views on discourse semantic patterns in text and corpus. Moreover, AppAnn visualizations are illustrated in a series of explorations of identity in a corpus of editorials and op-eds about the bin Laden killing. The findings suggest that the intriguing intricacies of discourse semantic meanings can be successfully discerned and more readily understood through linguistic visualization. The findings also provide insightful implications for discourse analysis by contributing to our understanding of a number of underdeveloped concepts of SFL, including coupling, commitment, instantiation, affiliation and individuation

Toward Visualization for Games: Theory, Design Space, and Patterns

Abstract-Electronic games are starting to incorporate in-game telemetry that collects data about player, team, and community performance on a massive scale, and as data begins to accumulate, so does the demand for effectively analyzing this data. In this paper, we use examples from both old and new games of different genres to explore the theory and design space of visualization for games. Drawing on these examples, we define a design space for this novel research topic and use it to formulate design patterns for how to best apply visualization technology to games. We then discuss the implications that this new framework will potentially have on the design and development of game and visualization technology in the future

The Songs of Our Past

Advancements in technology have resulted in unique changes in the way people interact with music today: Small, portable devices allow listening to it everywhere and provide access to thousands or, via streaming, even millions of songs. In addition, all played tracks can be logged with an accuracy down to the second. So far, these music listening histories are mostly used for music recommendation and hidden from their actual creators. But people may also benefit from this data more directly: as memory extensions that allow retrieving the name of a title, for rediscovering old favorites and reflecting about their lives. Additionally, listening histories can be representations of the implicit relationships between musical items. In this thesis, I discuss the contents of these listening histories and present software tools that give their owners the chance to work with them. As a first approach to understanding the patterns contained in listening histories I give an overview of the relevant literature from musicology, human-computer-interaction and music information retrieval. This literature review identifies the context as a main influence for listening: from the musical and temporal to the demographical and social. I then discuss music listening histories as digital memory extensions and a part of lifelogging data. Based on this notion, I present what an ideal listening history would look like and how close the real-world implementations come. I also derive a design space, centered around time, items and listeners, for this specific type of data and shortcomings of the real-world data regarding the previously identified contextual factors. The main part of this dissertation describes the design, implementation and evaluation of visualizations for listening histories. The first set of visualizations presents listening histories in the context of lifelogging, to allow analysing one’s behavior and reminiscing. These casual information visualizations vary in complexity and purpose. The second set is more concerned with the musical context and the idea that listening histories also represent relationships between musical items. I present approaches for improving music recommendation through interaction and integrating listening histories in regular media players. The main contributions of this thesis to HCI and information visualization are: First, a deeper understanding of relevant aspects and important patterns that make a person’s listening special and unique. Second, visualization prototypes and a design space of listening history visualizations that show approaches how to work with temporal personal data in a lifelogging context. Third, ways to improve recommender systems and existing software through the notion of seeing relationships between musical items in listening histories. Finally, as a meta-contribution, the casual approach of all visualizations also helps in providing non-experts with access to their own data, a future challenge for researchers and practitioners alike

Close and Distant Reading Visualizations for the Comparative Analysis of Digital Humanities Data

Traditionally, humanities scholars carrying out research on a specific or on multiple literary work(s) are interested in the analysis of related texts or text passages. But the digital age has opened possibilities for scholars to enhance their traditional workflows. Enabled by digitization projects, humanities scholars can nowadays reach a large number of digitized texts through web portals such as Google Books or Internet Archive. Digital editions exist also for ancient texts; notable examples are PHI Latin Texts and the Perseus Digital Library. This shift from reading a single book “on paper” to the possibility of browsing many digital texts is one of the origins and principal pillars of the digital humanities domain, which helps developing solutions to handle vast amounts of cultural heritage data – text being the main data type. In contrast to the traditional methods, the digital humanities allow to pose new research questions on cultural heritage datasets. Some of these questions can be answered with existent algorithms and tools provided by the computer science domain, but for other humanities questions scholars need to formulate new methods in collaboration with computer scientists. Developed in the late 1980s, the digital humanities primarily focused on designing standards to represent cultural heritage data such as the Text Encoding Initiative (TEI) for texts, and to aggregate, digitize and deliver data. In the last years, visualization techniques have gained more and more importance when it comes to analyzing data. For example, Saito introduced her 2010 digital humanities conference paper with: “In recent years, people have tended to be overwhelmed by a vast amount of information in various contexts. Therefore, arguments about ’Information Visualization’ as a method to make information easy to comprehend are more than understandable.” A major impulse for this trend was given by Franco Moretti. In 2005, he published the book “Graphs, Maps, Trees”, in which he proposes so-called distant reading approaches for textual data that steer the traditional way of approaching literature towards a completely new direction. Instead of reading texts in the traditional way – so-called close reading –, he invites to count, to graph and to map them. In other words, to visualize them. This dissertation presents novel close and distant reading visualization techniques for hitherto unsolved problems. Appropriate visualization techniques have been applied to support basic tasks, e.g., visualizing geospatial metadata to analyze the geographical distribution of cultural heritage data items or using tag clouds to illustrate textual statistics of a historical corpus. In contrast, this dissertation focuses on developing information visualization and visual analytics methods that support investigating research questions that require the comparative analysis of various digital humanities datasets. We first take a look at the state-of-the-art of existing close and distant reading visualizations that have been developed to support humanities scholars working with literary texts. We thereby provide a taxonomy of visualization methods applied to show various aspects of the underlying digital humanities data. We point out open challenges and we present our visualizations designed to support humanities scholars in comparatively analyzing historical datasets. In short, we present (1) GeoTemCo for the comparative visualization of geospatial-temporal data, (2) the two tag cloud designs TagPies and TagSpheres that comparatively visualize faceted textual summaries, (3) TextReuseGrid and TextReuseBrowser to explore re-used text passages among the texts of a corpus, (4) TRAViz for the visualization of textual variation between multiple text editions, and (5) the visual analytics system MusikerProfiling to detect similar musicians to a given musician of interest. Finally, we summarize our and the collaboration experiences of other visualization researchers to emphasize the ingredients required for a successful project in the digital humanities, and we take a look at future challenges in that research field

Using information visualization to support the self-management of type 2 diabetes mellitus

The globally increasing number of individuals suffering from Type 2 Diabetes Mellitus (T2DM), a completely preventable incurable disease of the pancreas, highlights the need for an effective tool for users to understand the relationship between their behaviours and the effect that those behaviours can have on their blood glucose levels (BGLs). There are few Information Visualisation (IV) tools available that can be used to reduce the cognition required to understand correlations between behaviour and BGLs. Existing tools require time-consuming, lengthy inputs and provide simple visualisations that do not show correlations. This leads to ineffective self-management of T2DM. Information Visualisation (IV) techniques can be used to support effective self-management of T2DM and reduce the cognition required to interpret DM data. Suitable IV techniques were identified and used to visualize T2DM data to aid in the self-management of the disease. Temporal charts, i.e. The Bar, Pie and Line Chart as well as heat maps, were selected as the most appropriate IV techniques to visualize T2DM data as they support time-series data well. A prototype, MedicMetric was created as an IV tool for visualizing T2DM data. MedicMetric incorporated three designed charts, namely the Change Rate Line View, the Radial Progress View, and the Annotated Line View. The Change Rate Line View and Annotated Line View both used line IV techniques, while the Radial Progress View made use of the bar IV technique. The Change Rate Line View performed the worst overall. A usability evaluation was conducted to compare these techniques and to determine which technique is most suitable for visualizing T2DM data. The results leaned significantly in favour of the Annotated Line View. This view is most similar to the line charts typically used in other IV tools. For this reason, the MedicMetric app was briefly compared to the MySygr and Diabetes:M application. In effectiveness and efficiency, MedicMetric and MySugr obtained almost identical results. However, participants indicated that MedicMetric supported their tasks using the Visual Information Seeking Mantra (VISM) the best overall, with 100% of participants stating that they would prefer to use the MedicMetric application. Several usability problems were identified with the IV techniques and they were addressed shortly after the study was complete. Overall participants were most satisfied with the Annotated Line View.Thesis (MSc) -- Faculty of Science, Computing Sciences, 202

Using information visualization to support the self-management of type 2 diabetes mellitus

The globally increasing number of individuals suffering from Type 2 Diabetes Mellitus (T2DM), a completely preventable incurable disease of the pancreas, highlights the need for an effective tool for users to understand the relationship between their behaviours and the effect that those behaviours can have on their blood glucose levels (BGLs). There are few Information Visualisation (IV) tools available that can be used to reduce the cognition required to understand correlations between behaviour and BGLs. Existing tools require time-consuming, lengthy inputs and provide simple visualisations that do not show correlations. This leads to ineffective self-management of T2DM. Information Visualisation (IV) techniques can be used to support effective self-management of T2DM and reduce the cognition required to interpret DM data. Suitable IV techniques were identified and used to visualize T2DM data to aid in the self-management of the disease. Temporal charts, i.e. The Bar, Pie and Line Chart as well as heat maps, were selected as the most appropriate IV techniques to visualize T2DM data as they support time-series data well. A prototype, MedicMetric was created as an IV tool for visualizing T2DM data. MedicMetric incorporated three designed charts, namely the Change Rate Line View, the Radial Progress View, and the Annotated Line View. The Change Rate Line View and Annotated Line View both used line IV techniques, while the Radial Progress View made use of the bar IV technique. The Change Rate Line View performed the worst overall. A usability evaluation was conducted to compare these techniques and to determine which technique is most suitable for visualizing T2DM data. The results leaned significantly in favour of the Annotated Line View. This view is most similar to the line charts typically used in other IV tools. For this reason, the MedicMetric app was briefly compared to the MySygr and Diabetes:M application. In effectiveness and efficiency, MedicMetric and MySugr obtained almost identical results. However, participants indicated that MedicMetric supported their tasks using the Visual Information Seeking Mantra (VISM) the best overall, with 100% of participants stating that they would prefer to use the MedicMetric application. Several usability problems were identified with the IV techniques and they were addressed shortly after the study was complete. Overall participants were most satisfied with the Annotated Line View.Thesis (MSc) -- Faculty of Science, Computing Sciences, 202

Voicing imperial subjects in British literature:a corpus analysis of literary dialect, 1768-1929

This study investigates nonstandard dialect as it used in fictional dialogue. The works included in it were produced by British authors between 1768 and 1929 – a period marking the expansion and height of the British Empire. One of the project’s aims is to examine the connections among dialect representation and the imperial project, to investigate how ventriloquizing African diasporic, Chinese, and Indian characters works with related forms of characterization to encode ideologies and relations of power. A related aim is to explore the emergence and evolution of these literary dialects over time and to compare their structures as they are used to impersonate different communities of speakers. In order to track such patterns of representation, a corpus was constructed from the dialogue of 126 novels, plays, and short stories. That dialogue was then annotated for more than 200 lexical, morphological, orthographic, and phonological features. That data enable statistical analyses that model variation in the voicing of speakers and how those voicings change over time. This modeling demonstrates, for example, an increase in the frequency of phonological features for African diasporic dialogue and a countervailing decrease in the frequency and complexity of coded features generally for Indian dialogue. Trends like these that are surfaced though quantitative methods are further contextualized using qualitative, archival data. The analysis ultimately rests on connecting patterns of representation to changes in the imperial political economy, evolving language ideologies that circulate in the Anglophone world, and shifts in sociocultural anxieties that crosscut race and empire. The combined quantitative and qualitative analyses, therefore, expose representational systems – the apparatuses that propagate structures and the social attitudes that accrue to those structures. It further demonstrates that in such propagation, structures and attitudes are complementary