Tips for Tracking Web Information Seeking Behavior

Tips for Tracking Web Information Seeking Behavior

Brian Detlor (McMaster University, Canada), Maureen Hupfer (McMaster University, Canada) and Umar Ruhi (University of Ottawa, Canada)
Copyright: © 2009 |Pages: 28
DOI: 10.4018/978-1-59904-974-8.ch013
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Abstract

This chapter provides various tips for practitioners and researchers who wish to track end-user Web information seeking behavior. These tips are derived in large part from the authors’ own experience of collecting and analyzing individual differences, task, and Web tracking data to investigate people’s online information seeking behaviors at a specific municipal community portal site (myhamilton.ca). The tips discussed in this chapter include: (1) the need to account for both task and individual differences in any Web information seeking behavior analysis; (2) how to collect Web metrics through deployment of a unique ID that links individual differences, task, and Web tracking data together; (3) the types of Web log metrics to collect; (4) how to go about collecting and making sense of such metrics; and (5) the importance of addressing privacy concerns at the start of any collection of Web tracking information.
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Introduction

Upon first consideration, employing Web tracking to better understand end-user experiences with the Web seems to be a simple process of installing the tracking software, collecting the data over a certain period of time, and conducting the analysis. However, our own experience in setting up, collecting, and analyzing Web tracking data has shown us that the process is surprisingly more difficult than originally expected.

To share what we have learned to help others set up and better utilize Web tracking tools, we have reflected upon what we believe are key tips concerning the use of Web tracking in any Web information seeking analysis. Thus, the overall purpose of this chapter is to discuss the practicalities and usefulness of collecting Web tracking data to help measure and assess the performance and usage of a Website or application, particularly with respect to Web information seeking.

Note that the ideas presented in this chapter are grounded in a research project conducted by the authors over the last three years that investigates people’s online behaviors at a municipal community portal site called myhamilton.ca (www.myhamilton.ca). The ultimate goal of the project is to understand the relationships among individual user characteristics such as demographics and personality traits, user attitudes toward and perceptions about accomplishing certain tasks (Web services) online, and actual usage behavior. We believe that an understanding of these relationships will provide insight into how characteristics of the individual, the task, and utilization behaviors affect task performance in an online community environment. We also believe that the capture and analysis of Web tracking data is imperative to reaching such an understanding.

The difficulty in utilizing Web tracking data successfully is in knowing how to position its collection and use within the larger confines of Web information seeking analysis. Web tracking is just one tool that needs to be coordinated with other data collection methods to yield a more comprehensive understanding than Web tracking alone could ultimately provide.

The objective of this chapter is to raise awareness of this point and to suggest techniques and approaches for the collection and analysis of Web tracking information that will aid practitioners in their performance measurement initiatives and understanding of how end-users seek information on the Web. Various tips are presented:

  • The need to account for both task and individual differences in any Web information seeking analysis assessment

  • The benefits of using a unique ID to link individual differences, task, and Web tracking data

  • The types of Web metrics to collect

  • How to gather and make sense of the Web metric information that is collected in Web logfiles

  • The importance of addressing privacy concerns right up-front in the collection of Web tracking information

We begin by providing background on the need to take both task and individual differences into consideration when investigating end-user Web information seeking behavior. To do this, we provide a general model that describes how task and individual differences affect information seeking behavior. Next, methods to conduct a Web information seeking analysis that allows for the collection of both task and individual differences data are presented. Importantly, these methods include the collection of Web tracking data via the use of Web logs. Using a selective subset of variables from the general model presented earlier, our own myhamilton.ca project serves as a point of illustration. We also provide details with respect to the types of Web metrics to collect and what needs to be done to make sense of these data. Finally, the importance of addressing privacy in any Web information seeking analysis is highlighted.

To help clarify things, find below the following definitions of terms:

Key Terms in this Chapter

Individual Differences: The demographic and psychological characteristics of people that distinguish one person from another.

Information Seeking Behavior: Refers to how people seek information in different contexts (Fisher, Erdelez & McKechnie, 2005).

Web Tracking: Refers to the automated collection of Web information seeking behavioral data.

Web Metrics: Pertains to the measures by which to assess a person’s Web information seeking behavior or to assess and monitor activity on a Website. Examples of commonly used Web metrics include page views, page transitions, and session times.

Task: In this chapter, refers to the information seeking task an individual user experiences that instills a need for information and motivates the user to satisfy this information need through some sort of information seeking behavior. Task is the context surrounding a person’s information need.

Web Information Seeking Behavior: Refers to information seeking behaviors that occur over the Web. Choo, Detlor & Turnbull (2000) identify four main modes of information seeking on the Web ranging from wayward browsing to goal-directed search (undirected viewing, conditioned viewing, informal search, and formal search) where each mode is characterized by predominant information seeking moves or activities (undirected viewing: starting and chaining; conditioned viewing: browsing and differentiating; informal search: differentiating, monitoring, and extracting; and formal search: monitoring and extracting).

Complete Chapter List

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Table of Contents
Preface
Bernard J. Jansen, Amanda Spink, Isak Taksa
Chapter 1
Bernard J. Jansen, Isak Taksa, Amanda Spink
This chapter outlines and discusses theoretical and methodological foundations for transaction log analysis. We first address the fundamentals of... Sample PDF
Research and Methodological Foundations of Transaction Log Analysis
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Chapter 2
W. David Penniman
This historical review of the birth and evolution of transaction log analysis applied to information retrieval systems provides two perspectives.... Sample PDF
Historic Perspective of Log Analysis
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Chapter 3
Lee Rainie, Bernard J. Jansen
Every research methodology for data collection has both strengths and limitations, and this is certainly true for transaction log analysis.... Sample PDF
Surveys as a Complementary Method for Web Log Analysis
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Chapter 4
Sam Ladner
This chapter aims to improve the rigor and legitimacy of Web-traffic measurement as a social research method. I compare two dominant forms of... Sample PDF
Watching the Web: An Ontological and Epistemological Critique of Web-Traffic Measurement
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Chapter 5
Kirstie Hawkey
This chapter examines two aspects of privacy concerns that must be considered when conducting studies that include the collection of Web logging... Sample PDF
Privacy Concerns for Web Logging Data
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Chapter 6
Bernard J. Jansen
Exploiting the data stored in search logs of Web search engines, Intranets, and Websites can provide important insights into understanding the... Sample PDF
The Methodology of Search Log Analysis
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Chapter 7
Anthony Ferrini, Jakki J. Mohr
As the Web’s popularity continues to grow and as new uses of the Web are developed, the importance of measuring the performance of a given Website... Sample PDF
Uses, Limitations, and Trends in Web Analytics
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Chapter 8
Danielle Booth
This chapter is an overview of the process of Web analytics for Websites. It outlines how visitor information such as number of visitors and visit... Sample PDF
A Review of Methodologies for Analyzing Websites
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Chapter 9
Gi Woong Yun
This chapter discusses validity of units of analysis of Web log data. First, Web log units are compared to the unit of analysis of television to... Sample PDF
The Unit of Analysis and the Validity of Web Log Data
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Chapter 10
Kirstie Hawkey, Melanie Kellar
This chapter presents recommendations for reporting context in studies of Web usage including Web browsing behavior. These recommendations consist... Sample PDF
Recommendations for Reporting Web Usage Studies
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Chapter 11
Seda Ozmutlu, Huseyin C. Ozmutlu, Amanda Spink
This chapter summarizes the progress of search engine user behavior analysis from search engine transaction log analysis to estimation of user... Sample PDF
From Analysis to Estimation of User Behavior
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Chapter 12
Gheorghe Muresan
In this chapter, we describe and discuss a methodological framework that integrates analysis of interaction logs with the conceptual design of the... Sample PDF
An Integrated Approach to Interaction Design and Log Analysis
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Chapter 13
Brian Detlor, Maureen Hupfer, Umar Ruhi
This chapter provides various tips for practitioners and researchers who wish to track end-user Web information seeking behavior. These tips are... Sample PDF
Tips for Tracking Web Information Seeking Behavior
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Chapter 14
Sandro José Rigo
Adaptive Hypermedia is an effective approach to automatic personalization that overcomes the difficulties and deficiencies of traditional Web... Sample PDF
Identifying Users Stereotypes for Dynamic Web Pages Customization
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Chapter 15
Brian K. Smith, Priya Sharma, Kyu Yon Lim, Goknur Kaplan Akilli, KyoungNa Kim, Toru Fujimoto
Computers and networking technologies have led to increases in the development and sustenance of online communities, and much research has focused... Sample PDF
Finding Meaning in Online, Very-Large Scale Conversations
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Chapter 16
Isak Taksa, Sarah Zelikovitz, Amanda Spink
Search query classification is a necessary step for a number of information retrieval tasks. This chapter presents an approach to non-hierarchical... Sample PDF
Machine Learning Approach to Search Query Classification
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Chapter 17
Seda Ozmutlu, Huseyin C. Ozmutlu, Amanda Spink
This chapter emphasizes topic analysis and identification of search engine user queries. Topic analysis and identification of queries is an... Sample PDF
Topic Analysis and Identification of Queries
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Chapter 18
Elmer V. Bernstam, Jorge R. Herskovic, William R. Hersh
Clinicians, researchers and members of the general public are increasingly using information technology to cope with the explosion in biomedical... Sample PDF
Query Log Analysis in Biomedicine
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Chapter 19
Michael Chau, Yan Lu, Xiao Fang, Christopher C. Yang
More non-English contents are now available on the World Wide Web and the number of non-English users on the Web is increasing. While it is... Sample PDF
Processing and Analysis of Search Query Logs in Chinese
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Chapter 20
Udo Kruschwitz, Nick Webb, Richard Sutcliffe
The theme of this chapter is the improvement of Information Retrieval and Question Answering systems by the analysis of query logs. Two case studies... Sample PDF
Query Log Analysis for Adaptive Dialogue-Driven Search
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Chapter 21
Mimi Zhang
In this chapter, we present the action-object pair approach as a conceptual framework for conducting transaction log analysis. We argue that there... Sample PDF
Using Action-Object Pairs as a Conceptual Framework for Transaction Log Analysis
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Chapter 22
Paul DiPerna
This chapter proposes a new theoretical construct for evaluating Websites that facilitate online social networks. The suggested model considers... Sample PDF
Analysis and Evaluation of the Connector Website
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Chapter 23
Marie-Francine Moens
This chapter introduces information extraction from blog texts. It argues that the classical techniques for information extraction that are commonly... Sample PDF
Information Extraction from Blogs
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Chapter 24
Adriana Andrade Braga
This chapter explores the possibilities and limitations of nethnography, an ethnographic approach applied to the study of online interactions... Sample PDF
Nethnography: A Naturalistic Approach Towards Online Interaction
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Chapter 25
Isak Taksa, Amanda Spink, Bernard J. Jansen
Web log analysis is an innovative and unique field constantly formed and changed by the convergence of various emerging Web technologies. Due to its... Sample PDF
Web Log Analysis: Diversity of Research Methodologies
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