Citation chain aggregation: An interaction model to support citation cycling

This is the postprint version of the conference paper.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

Visualising and animating visual information foraging in context

Optimal information foraging provides a potentially useful framework for modelling, analysing, and interpreting search strategies of users through a spatial-semantic interface. Improving the understanding of behavioural patterns of users in such environments has implications for the design and refinement of a range of user interfaces. In this article, we outline the role of optimal information foraging in the study of visual information retrieval and how one may use visualisation and animation techniques to put behavioural patterns in context. Behavioural patterns of information foraging in an information space are visualised and animated to aid further in-depth analysis of search strategies

Exploring cognitive issues in visual information retrieval

A study was conducted that compared user performance across a range of search tasks supported by both a textual and a visual information retrieval interface (VIRI). Test scores representing seven distinct cognitive abilities were examined in relation to user performance. Results indicate that, when using VIRIs, visual-perceptual abilities account for significant amounts of within-subjects variance, particularly when the relevance criteria were highly specific. Visualisation ability also seemed to be a critical factor when users were required to change topical perspective within the visualisation. Suggestions are made for navigational cues that may help to reduce the effects of these individual differences

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

A study of navigation strategies in spatial-semantic visualizations

Visualisations of abstract data are believed to assist the searcher by providing an overview of the semantic structure of a document collection whereby semantically similar items tend to cluster in space. Cribbin and Chen (2001) found that similarity data represented using minimum spanning tree (MST) graphs provided greater levels of support to users when conducting a range of information seeking tasks, in comparison to simple scatter graphs. MST graphs emphasise the most salient relationships between nodes by means of connecting links. This paper is based on the premise that it is the provision of these links that facilitated search performance. Using a combination of visual observations and existing theory, hypotheses predicting navigational strategies afforded by the MST link structure are presented and tested. The utility, in terms of navigational efficiency and retrieval success, of these and other observed strategies is then examined

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

Footprints of information foragers: Behaviour semantics of visual exploration

Social navigation exploits the knowledge and experience of peer users of information resources. A wide variety of visual–spatial approaches become increasingly popular as a means to optimize information access as well as to foster and sustain a virtual community among geographically distributed users. An information landscape is among the most appealing design options of representing and communicating the essence of distributed information resources to users. A fundamental and challenging issue is how an information landscape can be designed such that it will not only preserve the essence of the underlying information structure, but also accommodate the diversity of individual users. The majority of research in social navigation has been focusing on how to extract useful information from what is in common between users' profiles, their interests and preferences. In this article, we explore the role of modelling sequential behaviour patterns of users in augmenting social navigation in thematic landscapes. In particular, we compare and analyse the trails of individual users in thematic spaces along with their cognitive ability measures. We are interested in whether such trails can provide useful guidance for social navigation if they are embedded in a visual–spatial environment. Furthermore, we are interested in whether such information can help users to learn from each other, for example, from the ones who have been successful in retrieving documents. In this article, we first describe how users' trails in sessions of an experimental study of visual information retrieval can be characterized by Hidden Markov Models. Trails of users with the most successful retrieval performance are used to estimate parameters of such models. Optimal virtual trails generated from the models are visualized and animated as if they were actual trails of individual users in order to highlight behavioural patterns that may foster social navigation. The findings of the research will provide direct input to the design of social navigation systems as well as to enrich theories of social navigation in a wider context. These findings will lead to the further development and consolidation of a tightly coupled paradigm of spatial, semantic and social navigation

Discovering latent topical structure by second-order similarity analysis

This is the post-print of the Article - Copyright @ 2011 ASIS&TDocument similarity models are typically derived from a term-document vector space representation by comparing all vector-pairs using some similarity measure. Computing similarity directly from a ‘bag of words’ model can be problematic because term independence causes the relationships between synonymous and related terms and the contextual influences that determine the ‘sense’ of polysemous terms to be ignored. This paper compares two methods that potentially address these problems by modelling the higher-order relationships that lie latent within the original vector space. The first is latent semantic analysis (LSA), a dimension reduction method which 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, where similarity is measured in terms of profiles of first-order similarities as computed directly from the term-document space. Nearest neighbour tests show that SOS analysis produces similarity models that are consistently better than both first-order and LSA derived models at resolving both coarse and fine level semantic clusters. SOS analysis has been criticised for its cubic complexity. A second contribution is the novel application of vector truncation to reduce the run-time by a constant factor. Speed-ups of four to ten times are found to be easily achievable without losing the structural benefits associated with SOS analysis

Style over substance: A psychologically informed approach to feature selection and generalisability for author classification

Data availability: Data will be made available on request.Copyright © 2023 The Authors. Author profiling, or classifying user generated content based on demographic or other personal attributes, is a key task in social media-based research. Whilst high-accuracy has been achieved on many attributes, most studies tend to train and test models on a single domain only, ignoring cross-domain performance and research shows that models often transfer poorly into new domains as they tend to depend heavily on topic-specific (i.e., lexical) features. Knowledge specific to the field (e.g., Psychology, Political Science) is often ignored, with a reliance on data driven algorithms for feature development and selection. Focusing on political affiliation, we evaluate an approach that selects stylistic features according to known psychological correlates (personality traits) of this attribute. Training data was collected from Reddit posts made by regular users of the political subreddits of r/republican and r/democrat. A second, non-political dataset, was created by collecting posts by the same users but in different subreddits. Our results show that introducing domain specific knowledge in the form of psychologically informed stylistic features resulted in better out of training domain performance than lexical or more commonly used stylistic features

Have We Even Solved the First 'Big Data Challenge'?: Practical Issues Concerning Data Collection and Visual Representation for Social Media Analytics

Thanks to an influx of data collection and analytic software, harvesting and visualizing ‘big’ social media data1 is becoming increasingly feasible as a method for social science researchers. Yet while there is an emerging body of work utilizing social media as a data resource, there are a number of computational issues affecting data collection. These issues may problematize any conclusions we draw from our research work, yet for the large part, they remain hidden from the researcher’s view. We contribute towards the burgeoning literature which critically addresses various fundamental concerns with big data (see boyd and Crawford, 2012; Murthy, 2013; Rogers, 2013). However, rather than focusing on epistemological, political or theoretical issues — these areas are very ably accounted for by the authors listed above, and others — we engage with a different concern: how technical aspects of computational tools for capturing and handling social media data may impact our readings of it. This chapter outlines and explores two such technical issues as they occur for data taken from Twitter