Evaluation of storytelling in information visualization (MPhil to PhD Transfer Report)

Story telling has been used throughout the ages as a means of communication between people and to convey and transmit knowledge from one person to another, and from one generation to the next. In various domains, formulating of messages, ideas, or findings into a story has proven its efficiency in making them understandable, comprehensible, memorable, interesting, and engaging. Information Visualization as an academic field has also utilised the power of storytelling to make visualizations more understandable and interesting for a variety of audiences, including experts. However, although storytelling has been a hot topic in information visualization for some time, little or no empirical evaluations exist to compare different approaches of storytelling through information visualization. There is also a need for work that addresses in depth some particular criteria and techniques of storytelling such as transitions types in visual stories in general and data-driven stories in particular. A within subject experiment with 13 participants has been conducted to explore empirically how two different models of story delivery with information visualization influence narratives/stories constructed by audiences. Specifically, the first model involves direct narrative by a speaker using a visualization design to tell a story, while the second model involves constructing a story by interactively exploring visualization software. An openended questionnaire in controlled laboratory settings has been used in which the primary goal was to collect a number of stories derived from the two models. All the stories written by the participants were transcribed, analysed, and coded, using data-driven and preset themes. Themes included initial perception of the main story pattern/topic, insight types derived, narrative structures, and unexpected type of insights gained. This experiment was followed by a semi-structured interview where each participant answered two Likert-scale questions on each delivery model, and commented on the overall experiment. It is found that although most participants found telling a story easier with the first model (narrative) they did not perform better in other aspects. The second model (software) was advantegeous in the variety of insight types gained and participants accepted the message and information more neutrally. In contrast, participants were more critical about the data in software model than in the narrative model. The role of time in structuring story events was more apparent in the software model. These findings have some significant practical implications on storytelling through information visualization. A statement of the work done and a work plan for the remaining period of the PhD is also included explaining the proposed enhancement to the experiment conducted and further research work planned to address the issue of transitions in storytelling visualization

The effect of information visualization delivery on narrative construction and development

We conducted a within-subject experiment involving 13 participants that empirically explore how two different models of story delivery involving information visualization influence audience-constructed narratives. The first model involves a speaker using visualization software to communicate a direct narrative, while the second involves constructing a story by interactively exploring visualization software. We used an openended questionnaire in controlled laboratory settings, with the primary goal of collecting a number of stories derived from the two models, followed by two Likert-scale questions on the ease of telling and curiosity about the story in each delivery model. We qualitatively analysed the stories constructed by the participants, based on a number of themes tied to storytelling, including time and place and narrative structure. The study’s results reveal some interesting possible differences in how users receive, interpret, and create stories in each case

Embedding Spatial Software Visualization in the IDE: an Exploratory Study

Software visualization can be of great use for understanding and exploring a software system in an intuitive manner. Spatial representation of software is a promising approach of increasing interest. However, little is known about how developers interact with spatial visualizations that are embedded in the IDE. In this paper, we present a pilot study that explores the use of Software Cartography for program comprehension of an unknown system. We investigated whether developers establish a spatial memory of the system, whether clustering by topic offers a sound base layout, and how developers interact with maps. We report our results in the form of observations, hypotheses, and implications. Key findings are a) that developers made good use of the map to inspect search results and call graphs, and b) that developers found the base layout surprising and often confusing. We conclude with concrete advice for the design of embedded software maps.Comment: To appear in proceedings of SOFTVIS 2010 conferenc

Analyzing the Language of Food on Social Media

We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have most predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo-referenced heatmaps, semantics-preserving wordclouds and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.Comment: An extended abstract of this paper will appear in IEEE Big Data 201

Exploratory topic modeling with distributional semantics

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015