5,709 research outputs found
Towards Data-Driven Generation of Visualizations for Automatically Generated News Articles
Peer reviewe
Alexandria: Extensible Framework for Rapid Exploration of Social Media
The Alexandria system under development at IBM Research provides an
extensible framework and platform for supporting a variety of big-data
analytics and visualizations. The system is currently focused on enabling rapid
exploration of text-based social media data. The system provides tools to help
with constructing "domain models" (i.e., families of keywords and extractors to
enable focus on tweets and other social media documents relevant to a project),
to rapidly extract and segment the relevant social media and its authors, to
apply further analytics (such as finding trends and anomalous terms), and
visualizing the results. The system architecture is centered around a variety
of REST-based service APIs to enable flexible orchestration of the system
capabilities; these are especially useful to support knowledge-worker driven
iterative exploration of social phenomena. The architecture also enables rapid
integration of Alexandria capabilities with other social media analytics
system, as has been demonstrated through an integration with IBM Research's
SystemG. This paper describes a prototypical usage scenario for Alexandria,
along with the architecture and key underlying analytics.Comment: 8 page
Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-link Diagrams
Graph or network data are widely studied in both data mining and
visualization communities to review the relationship among different entities
and groups. The data facts derived from graph visual analysis are important to
help understand the social structures of complex data, especially for data
journalism. However, it is challenging for data journalists to discover graph
data facts and manually organize correlated facts around a meaningful topic due
to the complexity of graph data and the difficulty to interpret graph
narratives. Therefore, we present an automatic graph facts generation system,
Calliope-Net, which consists of a fact discovery module, a fact organization
module, and a visualization module. It creates annotated node-link diagrams
with facts automatically discovered and organized from network data. A novel
layout algorithm is designed to present meaningful and visually appealing
annotated graphs. We evaluate the proposed system with two case studies and an
in-lab user study. The results show that Calliope-Net can benefit users in
discovering and understanding graph data facts with visually pleasing annotated
visualizations
Text-based Spatial and Temporal Visualizations and their Applications in Visual Analytics
Textual labels are an essential part of most visualizations used in practice. However, these textual labels are mainly used to annotate other visualizations rather than being a central part of the visualization. Visualization researchers in areas like cartography and geovisualization have studied the combination of graphical features and textual labels to generate map based visualizations, but textual labels alone are not the primary focus in these representations. The idea of using symbols in visual representations and their interpretation as a quantity is gaining more traction. These types of representations are not only aesthetically appealing but also present new possibilities of encoding data. Such scenarios regularly arise while designing visual representations, where designers have to investigate feasibility of encoding information using symbols alone especially textual labels but the lack of readily available automated tools, and design guidelines makes it prohibitively expensive to experiment with such visualization designs. In order to address such challenges, this thesis presents the design and development of visual representations consisting entirely of text. These visual representations open up the possibility of encoding different types of spatial and temporal datasets. We report our results through two novel visualizations: typographic maps and text-based TextRiver visualization. Typographic maps merge text and spatial data into a visual representation where text alone forms the graphical features, mimicking the practices of human map makers. We also introduce methods to combine our automatic typographic maps technique with spatial datasets to generate thema-typographic maps where the properties of individual characters in the map are modified based on the underlying spatial data. Our TextRiver visualization is composed of collection of stream-like shapes consisting entirely of text where each stream represents thematic strength variations over time within a corpus. Such visualization enables additional ways to encode information contained in temporal datasets by modifying text attributes. We also conducted a usability evaluation to assess the potential value of our text-based TextRiver design
Mapping AI Arguments in Journalism Studies
This study investigates and suggests typologies for examining Artificial
Intelligence (AI) within the domains of journalism and mass communication
research. We aim to elucidate the seven distinct subfields of AI, which
encompass machine learning, natural language processing (NLP), speech
recognition, expert systems, planning, scheduling, optimization, robotics, and
computer vision, through the provision of concrete examples and practical
applications. The primary objective is to devise a structured framework that
can help AI researchers in the field of journalism. By comprehending the
operational principles of each subfield, scholars can enhance their ability to
focus on a specific facet when analyzing a particular research topic
Sporthesia: Augmenting Sports Videos Using Natural Language
Augmented sports videos, which combine visualizations and video effects to
present data in actual scenes, can communicate insights engagingly and thus
have been increasingly popular for sports enthusiasts around the world. Yet,
creating augmented sports videos remains a challenging task, requiring
considerable time and video editing skills. On the other hand, sports insights
are often communicated using natural language, such as in commentaries, oral
presentations, and articles, but usually lack visual cues. Thus, this work aims
to facilitate the creation of augmented sports videos by enabling analysts to
directly create visualizations embedded in videos using insights expressed in
natural language. To achieve this goal, we propose a three-step approach - 1)
detecting visualizable entities in the text, 2) mapping these entities into
visualizations, and 3) scheduling these visualizations to play with the video -
and analyzed 155 sports video clips and the accompanying commentaries for
accomplishing these steps. Informed by our analysis, we have designed and
implemented Sporthesia, a proof-of-concept system that takes racket-based
sports videos and textual commentaries as the input and outputs augmented
videos. We demonstrate Sporthesia's applicability in two exemplar scenarios,
i.e., authoring augmented sports videos using text and augmenting historical
sports videos based on auditory comments. A technical evaluation shows that
Sporthesia achieves high accuracy (F1-score of 0.9) in detecting visualizable
entities in the text. An expert evaluation with eight sports analysts suggests
high utility, effectiveness, and satisfaction with our language-driven
authoring method and provides insights for future improvement and
opportunities.Comment: 10 pages, IEEE VIS conferenc
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