Cathal Hoare

Information Foraging with a Proximity-Based Browsing Tool

This paper describes a user friendly, powerful information foraging tool. Document sets are presented through combinations of traditional ranked lists and 2-dimensional proximity-based visualisations, created by uniting graph-theoretic clustering and force-directed layout techniques, where article positions are determined by inter-document similarities. By using Gestalt principles and information encoding, the simple layout improves search efficiency by leveraging human cognitive strengths that have generally been under-utilised in commercial GUI development. In this paper, design and realisation of the layout technique are described in the context of an article browsing framework. Results of an indicative comparative laboratory study, which evaluates the client application – and in particular Graph-Theoretic Force-Directed (GTFD) visualisations against traditional search engine interfaces – are then presented. This study demonstrates the advantage of graphical presentations when browsing an article collection. Finally, potential improvements identified during the study are discussed, as are future directions for this approach to collection browsing

Application of session analysis to search interface design

Evaluations of search features used in digital library environments are generally results centric, focussing on the outcome of an evaluation - for example, the number of relevant documents retrieved - rather than garnering an understanding of why that result was achieved. This paper explores how search feature development benefits from user-centered evaluation. By examining the application of an established web analytics technique, session analysis, to the development of search features and interfaces, it will be shown that designers can better understand how users conduct evaluation tasks. The feedback provided by this technique allows for clearer evaluation of an interface and admits iteratively evolving designs that are based on empirical data.

The enhanced ranked list

Currently information-seeking interfaces treat each separate search query and result as a sequence of unrelated singletons rather than as a sequence of actions that inform one another as part of an information-seeking task. In order to successfully complete their search goals, the user must formulate and order their queries by applying a search strategy to the information problem. In reality, users often lack a feedback mechanism between disparate parts of the strategy. The objective of this paper is to explore how document sets can be used in pairs to explicitly support session-based information exploration.

Accurate classification of socially generated medical discourse

The growth of online health communities particularly those involving socially generated content can provide considerable value for society. Participants can gain knowledge of medical information or interact with peers on medical forum platforms. Analysing sentiment expressed by members of a health community in medical forum discourse can be of significant value, such as by identifying a particular aspect of an information space, determining themes that predominate among a large data set, and allowing people to summarize topics within a big data set. In this paper, we identify sentiments expressed in online medical forums that discuss Lyme disease. There are two goals in our research: first, to identify a complete and relevant set of categories that can characterize Lyme disease discourse; and second, to test and investigate strategies, both individually and collectively, for automating the classification of medical forum posts into those categories. We present a feature-based model that consists of three different feature sets: content-free, content-specific and meta-level features. Employing inductive learning algorithms to build a feature-based classification model, we assess the feasibility and accuracy of our automated classification. We further evaluate our model by assessing its ability to adapt to an online medical forum discussing Lupus disease. The experimental results demonstrate the effectiveness of our approach.

Multiclass Sentiment Classification of Online Health Forums using Both Domain-independent and Domain-specific Features

Online health-related discussion provides a rich source of information for both informing the public and providing feedback to health professionals to detect trends and inform policy. However, there are few studies that focus on analysing sentiment in medical forum discourse. Online health communities devoted to specific medical conditions and health-related problems support people with similar conditions, enabling them to exchange personal experiences. Analysing sentiment expressed by members of a health community in medical forum discourse can be valuable for identifying a particular aspect of the information space. In this paper, we identify sentiments expressed on online medical forums discussing Lyme disease. There are two goals in our research. First, to identify a set of categories that can represent a comprehensive connotation of emotions expressed in the discussions, while also being adequately distinct for the purposes of machine learning. Second, to identify the sentiments expressed by participants in individual posts. Three types of feature (content-free, content-specific and meta-level) are extracted and inductive learning algorithms utilized to build a feature-based classification model for an automated multi-class classification model. The experimental results demonstrate the effectiveness of our approach.

Collaborating through Real-Time VoIP and Visual Search

This paper describes the importance of allowing human-human collaboration in the information-seeking task by modeling the task using Charnov’s Marginal Gain Theorem. Highlighting areas of potential gain, the paper then describes an application that exploits those areas. The application described takes an existing information seeking tool – SolonEvo – and integrates it with a free point-2-point (P2P) Voice-over-IP (VoIP) tool by defining a set of protocols for message passing and providing an interface between the two applications that implement those protocols. The protocol and application are described.

Real-time collaboration through visual search and voice-over-IP

Numerous digital libraries (DLs), electronic archives (EAs) and portal services have been developed. These services allow online structured access to digitised information, facilitating remote access for educators and students. Often, DL users and information are remotely located - so too are their users. The authors can envision numerous circumstances where two remotely located parties may wish to opportunistically examine an online resource e-learning environments for example. We are particularly interested in assisting users whose collaboration resolves around discussion of a common visual resource (documents and collections of documents in the case under discussion). By providing a single tool for information seeking and multi-user collaboration, we believe that the amount of preparation required for an online session is reduced, while the flexibility allowed to parties to conduct ad-hoc examinations of a resource is increased. This paper proposes a framework to address this functionality deficit by describing a document foraging tool that provides facilities for both visual exploration of a document set and voice-over-IP (VoIP) based collaborative features

An information foraging tool

Electronic document repositories continue to expand rapidly; public collections, for instance the Google index, contain up to 8 billion individual items. Private electronic archives, maintained by companies, governments and other bodies grow at similar rates. While search techniques have scaled to manage these vast collections, most interfaces between search engines and searchers, usually based on a ranked list, are increasingly insufficient. This paper explains how Information Foraging Theory was applied to create visualisations of query resultsets which, when embedded in an application that contained tools to manipulate the visualisation, helped alleviate the deficiencies of the ranked list.