Timothy Cribbin

Visualizing and tracking the growth of competing paradigms: two case studies

In this article we demonstrate the use of an integrative approach to visualizing and tracking the development of scientific paradigms. This approach is designed to reveal the long-term process of competing scientific paradigms. We assume that a cluster of highly cited and cocited scientific publications in a cocitation network represents the core of a predominant scientific paradigm. The growth of a paradigm is depicted and animated through the rise of citation rates and the movement of its core cluster towards the center of the cocitation network. We study two cases of competing scientific paradigms in the real world: (1) the causes of mass extinctions, and (2) the connections between mad cow disease and a new variant of a brain disease in humans — vCJD. Various theoretical and practical issues concerning this approach are discussed.

Mapping semantic information in virtual space

This paper reports two studies investigating the computer-based representation of the semantic information content of databases using object location in two- and three-dimensional virtual space. In the first study, the cognitive demands associated with performing an information search task were examined under conditions where the “goodness of fit” of the spatial-semantic “mapping” was manipulated. The effects of individual differences in spatial ability and associative memory ability also were considered. Results indicated that performance equivalence, between two- and three-dimensional interfaces, could be achieved when the two-dimensional interface accounted for between 50 and 70% of the semantic variance accounted for by the three-dimensional solution. A second study, in which automatic text analysis was used to generate two- and three-dimensional solutions for document sets of varying sizes and types, supported the conclusion that, for the purpose of information search, the amount of additional semantic information that can be conveyed by a three-dimensional solution does not outweigh the associated additional cognitive demands.

Doing social media analytics

In the few years since the advent of ‘Big Data’ research, social media analytics has begun to accumulate studies drawing on social media as a resource and tool for research work. Yet, there has been relatively little attention paid to the development of methodologies for handling this kind of data. The few works that exist in this area often reflect upon the implications of ‘grand’ social science methodological concepts for new social media research (i.e. they focus on general issues such as sampling, data validity, ethics, etc.). By contrast, we advance an abductively oriented methodological suite designed to explore the construction of phenomena played out through social media. To do this, we use a software tool – Chorus – to illustrate a visual analytic approach to data. Informed by visual analytic principles, we posit a two-by-two methodological model of social media analytics, combining two data collection strategies with two analytic modes. We go on to demonstrate each of these four approaches ‘in action’, to help clarify how and why they might be used to address various research questions.

Cognitive ability and information retrieval: When less is more

This paper is concerned with the use of virtual environments as a means of conveying semantic information relating to the contents of computerised textual databases. two empirical studies are reported that investigated the influence of individual differences in cognitive ability on search task performance. In the first experiment, objects (each representing a type of animal) were placed ordinally in a three-dimensional cube arrangement based on ratings of semantic similarity. Participants were required to locate a series of randomly selected objects. Contrary to prediction, participants with high associative memory were comparatively poorer performers. In a second experiment ‘true’ rating distances were used to locate objects in virtual space. High spatial ability was associated with better performance and, in contrast with the results of Experiment 1, this pattern also was replicated for associative memory. Implications are discussed.

Discovering latent topical structure by second-order similarity analysis

Computing document similarity directly from a “bag of words” vector space model can be problematic because term independence causes the relationships between synonymous terms and the contextual influences that determine the sense of polysemous terms to be ignored. This study compares two methods that potentially address these problems by deriving the higher order relationships that lie latent within the original first-order space. The first is latent semantic analysis (LSA), a dimension reduction method that is a well-known means of addressing the vocabulary mismatch problem in information retrieval systems. The second is the lesser known yet conceptually simple approach of second-order similarity (SOS) analysis, whereby latent similarity is measured in terms of mutual first-order similarity. Nearest neighbour tests show that SOS analysis derives similarity models that are superior to both first-order and LSA-derived models at both coarse and fine levels of semantic granularity. SOS analysis has been criticized for its computational complexity. A second contribution is the novel application of vector truncation to reduce run-time by a constant factor. Speed-ups of 4 to 10 times are achievable without compromising the structural gains achieved by full-vector SOS analysis.

An investigation of visual cues used to create and support frames of reference and Visual search tasks in Desktop virtual environments

Visual depth cues are combined to produce the essential depth and dimensionality of Desktop Virtual Environments (DVEs). This study discusses DVEs in terms of the visual depth cues that create and support perception of frames of references and accomplishment of visual search tasks. This paper presents the results of an investigation that identifies the effects of the experimental stimuli positions and visual depth cues: luminance, texture, relative height and motion parallax on precise depth judgements made within a DVE. Results indicate that the experimental stimuli positions significantly affect precise depth judgements, texture is only significantly effective for certain conditions, and motion parallax, in line with previous results, is inconclusive to determine depth judgement accuracy for egocentrically viewed DVEs. Results also show that exocentric views, incorporating relative height and motion parallax visual cues, are effective for precise depth judgements made in DVEs. The results help us to understand the effects of certain visual depth cues to support the perception of frames of references and precise depth judgements, suggesting that the visual depth cues employed to create frames of references in DVEs may influence how effectively precise depth judgements are undertaken.

Citation chain aggregation: an interaction model to support citation cycling

Citation chaining is a powerful means of exploring the academic literature. Starting from just one or two known relevant items, a naïve researcher can cycle backwards and forwards through the citation graph to generate a rich overview of key works, authors and journals relating to their topic. Whilst online citation indexes greatly facilitate this process, the size and complexity of the search space can rapidly escalate. In this paper, we propose a novel interaction model called citation chain aggregation (CCA). CCA employs a simple three-list view which highlights the overlaps that occur between the first-generation relations of known relevant items. As more relevant articles are identified, differences in the frequencies of citations made by or to unseen articles provide strong relevance feedback cues. The benefits of this technique are illustrated using a simple case study.

An Interactive Method for Inferring Demographic Attributes in Twitter

Twitter data offers an unprecedented opportunity to study demographic differences in public opinion across a virtually unlimited range of subjects. Whilst demographic attributes are often implied within user data, they are not always easily identified using computational methods. In this paper, we present a semi-automatic solution that combines automatic classification methods with a user interface designed to enable rapid resolution of ambiguous cases. TweetClass employs a two-step, interactive process to support the determination of gender and age attributes. At each step, the user is presented with feedback on the confidence levels of the automated analysis and can choose to refine ambiguous cases by examining key profile and content data. We describe how a user-centered design approach was used to optimise the interface and present the results of an evaluation which suggests that TweetClass can be used to rapidly boost demographic sample sizes in situations where high accuracy is required.

Facilitating insight into a simulation model using visualization and dynamic model previews

This paper shows how model simplification, by replacing iterative steps with unitary predictive equations, can enable dynamic interaction with a complex simulation process. Model previews extend the techniques of dynamic querying and query previews into the context of ad hoc simulation model exploration. A case study is presented within the domain of counter-current chromatography. The relatively novel method of insight evaluation was applied, given the exploratory nature of the task. The evaluation data show that the trade-off in accuracy is far outweighed by benefits of dynamic interaction. The number of insights gained using the enhanced interactive version of the computer model was more than six times higher than the number of insights gained using the basic version of the model. There was also a trend for dynamic interaction to facilitate insights of greater domain importance.