Maoyuan Sun

Abdul Rahman Shaikh, Hamed Alhoori, Maoyuan Sun

Video communication has been rapidly increasing over the past decade, with YouTube providing a medium where users can post, discover, share, and react to videos. There has also been an increase in the number of videos citing research articles, especially since it has become relatively commonplace for academic conferences to require video submissions. However, the relationship between research articles and YouTube videos is not clear, and the purpose of the present paper is to address this issue. We created new datasets using YouTube videos and mentions of research articles on various online platforms. We found that most of the articles cited in the videos are related to medicine and biochemistry. We analyzed these datasets through statistical techniques and visualization, and built machine learning models to predict (1) whether a research article is cited in videos, (2) whether a research article cited in a video achieves a level of popularity, and (3) whether a video citing a research article becomes popular. The best models achieved F1 scores between 80% and 94%. According to our results, research articles mentioned in more tweets and news coverage have a higher chance of receiving video citations. We also found that video views are important for predicting citations and increasing research articles' popularity and public engagement with science.

Joshua Boley, Maoyuan Sun

User trust is a crucial consideration in designing robust visual analytics systems that can guide users to reasonably sound conclusions despite inevitable biases and other uncertainties introduced by the human, the machine, and the data sources which paint the canvas upon which knowledge emerges. A multitude of factors emerge upon studied consideration which introduce considerable complexity and exacerbate our understanding of how trust relationships evolve in visual analytics systems, much as they do in intelligent sociotechnical systems. A visual analytics system, however, does not by its nature provoke exactly the same phenomena as its simpler cousins, nor are the phenomena necessarily of the same exact kind. Regardless, both application domains present the same root causes from which the need for trustworthiness arises: Uncertainty and the assumption of risk. In addition, visual analytics systems, even more than the intelligent systems which (traditionally) tend to be closed to direct human input and direction during processing, are influenced by a multitude of cognitive biases that further exacerbate an accounting of the uncertainties that may afflict the user's confidence, and ultimately trust in the system. In this article we argue that accounting for the propagation of uncertainty from data sources all the way through extraction of information and hypothesis testing is necessary to understand how user trust in a visual analytics system evolves over its lifecycle, and that the analyst's selection of visualization parameters affords us a simple means to capture the interactions between uncertainty and cognitive bias as a function of the attributes of the search tasks the analyst executes while evaluating explanations. We sample a broad cross-section of the literature from visual analytics, human cognitive theory, and uncertainty, and attempt to synthesize a useful perspective.

Maoyuan Sun, Yue Ma, Yuanxin Wang et al.

Data-driven decision making has been a common task in today's big data era, from simple choices such as finding a fast way to drive home, to complex decisions on medical treatment. It is often supported by visual analytics. For various reasons (e.g., system failure, interrupted network, intentional information hiding, or bias), visual analytics for sensemaking of data involves missingness (e.g., data loss and incomplete analysis), which impacts human decisions. For example, missing data can cost a business millions of dollars, and failing to recognize key evidence can put an innocent person in jail. Being aware of missingness is critical to avoid such catastrophes. To fulfill this, as an initial step, we consider missingness in visual analytics from two aspects: data-centric and human-centric. The former emphasizes missingness in three data-related categories: data composition, data relationship, and data usage. The latter focuses on the human-perceived missingness at three levels: observed-level, inferred-level, and ignored-level. Based on them, we discuss possible roles of visualizations for handling missingness, and conclude our discussion with future research opportunities.

Maoyuan Sun, Abdul Rahman Shaikh, Hamed Alhoori et al.

Multiple-view visualization (MV) has been heavily used in visual analysis tools for sensemaking of data in various domains (e.g., bioinformatics, cybersecurity and text analytics). One common task of visual analysis with multiple views is to relate data across different views. For example, to identify threats, an intelligence analyst needs to link people from a social network graph with locations on a crime-map, and then search for and read relevant documents. Currently, exploring cross-view data relationships heavily relies on view-coordination techniques (e.g., brushing and linking), which may require significant user effort on many trial-and-error attempts, such as repetitiously selecting elements in one view, and then observing and following elements highlighted in other views. To address this, we present SightBi, a visual analytics approach for supporting cross-view data relationship explorations. We discuss the design rationale of SightBi in detail, with identified user tasks regarding the use of cross-view data relationships. SightBi formalizes cross-view data relationships as biclusters, computes them from a dataset, and uses a bi-context design that highlights creating stand-alone relationship-views. This helps preserve existing views and offers an overview of cross-view data relationships to guide user exploration. Moreover, SightBi allows users to interactively manage the layout of multiple views by using newly created relationship-views. With a usage scenario, we demonstrate the usefulness of SightBi for sensemaking of cross-view data relationships.

Hao Wu, Maoyuan Sun, Peng Mi et al.

Modern visual analytic tools promote human-in-the-loop analysis but are limited in their ability to direct the user toward interesting and promising directions of study. This problem is especially acute when the analysis task is exploratory in nature, e.g., the discovery of potentially coordinated relationships in massive text datasets. Such tasks are very common in domains like intelligence analysis and security forensics where the goal is to uncover surprising coalitions bridging multiple types of relations. We introduce new maximum entropy models to discover surprising chains of relationships leveraging count data about entity occurrences in documents. These models are embedded in a visual analytic system called MERCER that treats relationship bundles as first class objects and directs the user toward promising lines of inquiry. We demonstrate how user input can judiciously direct analysis toward valid conclusions whereas a purely algorithmic approach could be led astray. Experimental results on both synthetic and real datasets from the intelligence community are presented.