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

Chen, C

Cribbin, T

English

Brunel University Research Archive

VISUALISING AND ANIMATING VISUAL INFORMATION FORAGING INCONTEXTChaomei Chen, Timothy CribbinThe VIVID Research CentreDepartment of Information Systems and ComputingBrunel UniversityUxbridge UB8 3PH, UKTel: +44 1895 203080E-mail: {chaomei.chen, timothy.cribbin}@brunel.ac.ukABSTRACTOptimal information foraging provides a potentially useful framework for modelling, analysing, and interpretingsearch strategies of users through a spatial-semantic interface. Improving the understanding of behavioural patterns ofusers 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 usevisualisation and animation techniques to put behavioural patterns in context. Behavioural patterns of informationforaging in an information space are visualised and animated to aid further in-depth analysis of search strategies.1.   INTRODUCTIONAccording to the optimal information foraging theory (Pirolli & Card., 1995), searching for information is, in manyways, very much like hunting. The most fundamentally common strategy between the two focuses on the trade-offbetween the profitability of a target and the risk involved in achieving the goal at hand (Figure 1). When we searchthe Internet, we may consider the downloading time as a factor if we are using a modem and the size of the file to bedownloaded is huge.Figure 1. Information foraging focuses on risks as well as the profitability of a target.The key to the analysis of an optimal information foraging process is therefore similar to the analysis of a traditionalfood foraging process several thousands of years ago. It is the judgement of the profitability versus the risk thatdetermines one’s search strategy, search trails, and other behavioural patterns. When we deal with an informationspace, we can estimate the profitability of a document in terms of its intrinsic values, such as relevancy to our tasks athand. And we can estimate the risk of locating and retrieval a document in terms of our subscription fees, per itempurchase fees, waiting time, or phone bills for Internet connections.If we conceptualise a visual information retrieval process as information foraging, we may gain additional insightsinto users’ search strategies and their behavioural patterns in the context of spatially and semantically organisedinformation spaces. For example, one can estimate the profitability with the proportion of relevant documents in aspecific area of an information space divided by the time it will take to read all the documents within this area. In theirstudy of the Scatter/Gatherer system, Pirolli and Card found that even a simplified model of information foragingshows how users’ search strategies can be influenced. Their study suggests that users are likely to search widely in an-------------------------------------------------------------------------------------------------------------------------This is a post-print copy. For further reprinting or re-use, please contact the publisher: Lawrence Erlbaum Associates, Inc. at : http://www.routledge.com/information space if the query is simple, and more focused if the query is harder (1995). According to the profitabilityprinciple, harder queries entail higher cost to resolve and the profitability of each document is relatively low. Ingeneral, users must decide whether or not to pursue a given document on the course of navigation based on thelikelihood profitability of the document.The optimal information foraging theory provides a useful framework for the modelling and analysis of behaviouralpatterns involving visual navigation. In particular, users are concerned about how to maximise the potentialprofitability, where to search, and how long to search in a specific area.In this article, we present a conceptual framework for modelling behavioural patterns in visual information foraging.In particular, this visual information foraging takes place in a spatial-semantic interface of a thematic informationspace. This framework constitutes the optimal information foraging theory, Hidden Markov Models, spatial-semanticinterfaces, and a taxonomy of visual navigation. In this article, we focus on the role of information foraging in ourapproach. A related study focusing on Hidden Markov Models is reported in (Chen, Cribbin, Kuljis, & Macredie,2000). The overall approach is illustrated through an example in which visual navigation data were drawn from aninformation retrieval experiment. Finally, implications of this approach for understanding users’ navigation strategiesare discussed.2.   INFORMATION FORAGING IN VISUAL NAVIGATIONIn order to study users’ information foraging behaviour, we constructed four thematic spaces based on news articlesfrom the Los Angeles Times newspaper retrieved from the Text Retrieval Conference (TREC) test data. Eachthematic space contains the top 200 news articles retrieved through a single keyword query to the documentcollection. The four keywords used were alcohol, endangered, game, and storm. Corresponding spaces were namedaccordingly by these keywords.The user interface for each thematic space is designed based on a popular organisation principle, which is closelyconnected to Gestalt laws in cognitive psychology. Gestalt laws are several principles developed in earlier 20thcentury concerning how people perceive patterns. For example, the proximity principle says that people tend to seenatural groupings purely based on proximity relationships displayed (Figure 2). In this article, we focus on searchingbehaviours associated with spatial-semantic interfaces designed based on relevant Gestalt laws. We expect that users’search behaviours should reflect this spatial-semantic feature.Figure 2. A spatial-semantic mapping exploits the Gestalt law of proximity.For each thematic space, we generated a document-to-document similarity matrix using Latent Semantic Indexing(LSI) (Chen & Czerwinski, 1998; Deerwester, Dumais, Landauer, Furnas, & Harshman, 1990). A few types ofspatial-semantic interfaces were produced, including Pathfinder networks (PF) and minimum spanning trees (MST)(see Figure 3). In MST-based visualisation, N-1 explicit links connect all the documents together. Users can see theselinks on their screen. In PF-based visualisation, additional explicit links are allowed as long as the triangularinequality condition is satisfied. Detailed descriptions of the use of these techniques for information visualisation canbe found in (Chen, 1999).Figure 3: A spatial-semantic interface of the ALCOHOL thematic space.Users in this study are self-selected students at Brunel University. They performed a series of tasks in each thematicspace through spatial-semantic interfaces. Usage data was logged to a computer file in each session, including theoccurrence of an event, the time stamp of the event, and the target document on which the event takes place.The design of the tasks follows Shneiderman’s mantra: overview, zoom, filter, details on demand, which highlightsusers’ cognitive needs at various strategic stages in visual information retrieval. At the top level with Task A, usersneed to locate and mark documents relevant to a search topic (Figure 3). For example, users were asked to locate andmark any documents that mention an incident of drink driving. 20~25 documents were judged as relevant by expertsfor TREC conferences. Task B is more specific than Task A, and so on. We expect that users would narrow down thescope of their search from Task A through Task D and that this should be evident in their trails of navigation.Figure 4. Shneiderman’s mantra is used in the characterisation of interactive tasks.The screen display is designed as follows. Documents in red are not relevant to the search tasks. The course ofnavigation appears as dotted yellow links. Relevancy judgements made by experts are provided in the TREC test data.Documents relevant to the original search are marked with a bright yellow dot in the centre. If the user marks adocument as relevant in a search session, this document will be coloured in blue. Upon the user visits a document, adark circle is drawn around the current document. The time spent on a document is denoted by a growing green beltuntil the user leaves the document. If the user comes back to a previously visited document, we will see a new layer ofdark circle and an additional layer of green belt will start to be drawn. One can choose to carry these discs grown fromone task into the next task and a red disc indicates how long the user has spent on it in the previous task.Based on the above reasoning, we expect to observe the following patterns concerning users’ navigation strategies:1. Spatial-semantic models may reduce the time spent on examining a cluster of documents if the spatial-semantic mapping preserves the latent semantic structure.2. Spatial-semantic models may mislead information foragers to over-estimate the profitability of a clusterof documents if the quality of clustering is low.3. Once users locate a relevant document in a spatial-semantic model, they tend to switch to local search.4. If we use the radius of disc to denote the time spent on a document, the majority of large discs should fallin the target area in the thematic spaces.5. Discs of subsequent tasks are likely to be embedded in discs of preceding tasks.4.   RESULTSFigure 5 shows user jbr’s navigation trail for Task A in the ALCOHOL space, who performed the best in this group.Task A corresponds to the initial overview task in Shneiderman’s taxonomy. Users must locate clusters of relevantdocuments in the map. Subsequent tasks are increasingly focused.As shown in the trajectory map, user jbr started from the node 57 and moved downwards along the branch. Then thetrajectory jumped to node 105 and followed the long spine of the graph. Finally, the user reached the area whererelevant documents are located. We found an interesting trajectory pattern - once the user locates a relevantdocument, he tends to explore documents in the immediate neighbouring area. This confirmed our expectation. Thefrequency of long-range jumps across the space decreased as the user became familiar with the structure of the space.The trajectory eventually settled to some fine-grained local search within an area where the majority relevantdocuments are placed, and it didn’t move away from that area ever since, which was also what we expected.Figure 5: User jbr’s trails in searching the ALCOHOL space (Task A).In the trajectory replay, the time spent on a document is animated as the radius of a green disc growing outward fromwhere the document is located. This design allows us to find out whether the majority of large green discs appear inareas with a high density of relevant documents, and whether areas with a low density of relevant documents will onlyhave sporadic passing navigation trails.5.   DISCUSSION AND CONCLUSIONThis is an integrated approach to the study of behavioural semantics. The thematic space was exposed to users for thefirst time in Task A. Apart from the structural model, no navigation cues were readily available to users. Users mustfirst locate areas in the thematic space where they can find documents relevant to the task. The optimal informationforaging theory provides an appropriate description of this type of processes.Our framework allows us to introduce descriptive and normative modelling techniques. We have conducted an in-depth study of visual information foraging strategies utilising Hidden Markov Models (HMMs) (Chen et al., 2000).The visual inspection of information foraging trails is encouraging. Animated trails and optimal paths generated byHMMs have revealed many insights into how users were dealing with the tasks and what are the prevailingcharacteristics and patterns. Replay and animate HMM-paths over actual trails allow us to compare transition patternsin the same context.In conclusion, many of our expectations have been confirmed in the visualisation and animation of trails ofinformation foragers in thematic spaces. The task we have studies is a global information foraging in nature. Theinitial result is promising, especially with the facilities to animate user trails within the thematic spaces. Future studiesshould expand the scope of tasks to cover a fuller range of information foraging activities. Visual-spatial interfacesshould be carefully designed for future studies so that fundamental issues can be addressed.ACKNOWLEDGEMENTSThe work was supported in part by the Council for Museums, Archives and Library in the UK (RE/089).REFERENCESChen, C. (1999). Information Visualisation and Virtual Environments. London: Springer-Verlag London.Chen, C., Cribbin, T., Kuljis, J., & Macredie, R. (2000). Footprints of information foragers: Behaviour semantics ofvisual exploration. (Submitted for Publication).Chen, C., & Czerwinski, M. (1998, June 1998). From latent semantics to spatial hypertext: An integrated approach.Paper presented at the 9th ACM Conference on Hypertext and Hypermedia (Hypertext ’98), Pittsburgh, PA.Deerwester, S., Dumais, S. T., Landauer, T. K., Furnas, G. W., & Harshman, R. A. (1990). Indexing by LatentSemantic Analysis. Journal of the American Society for Information Science, 41(6), 391-407.Pirolli, P., & Card., S. K. (1995). Information foraging in information access environments. Paper presented at theConference on Human Factors in Computing (CHI ’95), Denver, CO.

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