Finding Meaning in Online, Very-Large Scale Conversations

Finding Meaning in Online, Very-Large Scale Conversations

Brian K. Smith (Pennsylvania State University, USA), Priya Sharma (Pennsylvania State University, USA), Kyu Yon Lim (Pennsylvania State University, USA), Goknur Kaplan Akilli (Pennsylvania State University, USA), KyoungNa Kim (Pennsylvania State University, USA) and Toru Fujimoto (Pennsylvania State University, USA)
Copyright: © 2009 |Pages: 21
DOI: 10.4018/978-1-59904-974-8.ch015
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Abstract

Computers and networking technologies have led to increases in the development and sustenance of online communities, and much research has focused on examining the formation of and interactions within these virtual communities. The methods for collecting data and analyzing virtual online communities, especially very large-scale online discussion forums can be varied and complex. In this chapter, we describe two analytical methods—qualitative data analysis and Social Network Analysis (SNA)–that we used to examine conversations within ESPN’s Fast Break community, which focuses on fantasy basketball sports games. Two different levels of analyses—the individual and community level—allowed us to examine individual reflection on game strategy and decision-making as well as characteristics of the community and patterns of interactions between participants within community. The description of our use of these two analytical methods can help researchers and designers who may be attempting to analyze and characterize other large-scale virtual communities.
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Introduction

The use of computer media to support collaboration and communication has increased in recent years. Electronic mail, instant messaging (IM), chat rooms, discussion forums, and social networking platforms (e.g., Facebook, MySpace, Twitter) all support people in conversing with others regardless of geographic and/or temporal proximity. Understanding how people come together to form online or virtual communities and how knowledge flows between participants over time has been a concern for researchers since the early days of computer-mediated collaboration (Curtis, 1992; Hiltz, 1985; Rheingold, 1995). Studies of online communities span many research questions from why people engage in them (Ridings, 2004) to content analyses of specific interests forums such as breast cancer (e.g., Rodgers & Chen, 2005; Sharf, 1997) and teacher professional development (e.g., Barab, MaKinster, & Scheckler, 2004; Renninger & Shumar, 2002; Schlager, Fusco, & Schank, 2002).

This chapter describes methods to collect and analyze conversations associated with such online, virtual communities, especially those that can be described as very large-scale conversations (VLSCs). Sack (2002) describes three characteristics of these online spaces:

  • 1.

    Size. VLSCs involve interchanges betweens hundreds and thousands of people. Newsgroups, chat forums, and Weblogs are examples of spaces where the volume of messages posted can range in the tens and hundreds of thousands.

  • 2.

    Networked communities. VLSCs support network-based communities that have few, if any, geographic and/or temporal boundaries. Individuals within these communities come together over similar interests rather than spatial concerns one might find in neighborhoods and cities. It is also clear that when actors, their activities, and the places where these occur are closely examined, they provide evidence that these communities are complex and multifaceted structures (Schweir, 2001; Wellman & Gulia, 1999; Wenger, 1998).

  • 3.

    Public. Conversations can occur between many individuals behind closed walls, e.g., a company's employees working on a major project. But many VLSCs are open and accessible to anyone. These are particularly interesting because individuals choosing to contribute to them are likely to be engaged in the conversation topic, enough that they are willing to expend long periods of time and effort in exchanges with others.

These three properties make VLSCs interesting artifacts for research. First, because these networked conversations typically revolve around specific topics and interests, their content can be analyzed to understand how people use, express, and learn knowledge over time. Their public nature makes them accessible to researchers. And their size provides opportunities for large conversational studies that could be difficult to collect and analyze in other media (e.g., face-to-face conversations).

Key Terms in this Chapter

Social Network Analysis (SNA): is a technique used to study the interactions between individuals in a community.

Ethnography: is a methodological technique for examining and understanding community life.

Density: describes the general level of linkage among the actors in a social network.

Very Large-Scale Conversations (VLSCs): are those that involve interchanges betweens hundreds and thousands of people. Newsgroups, chat forums, and Weblogs are examples of spaces where the volume of messages posted can range in the tens and hundreds of thousands.

Online or Virtual Communities: are sets of people that interact primarily using information communication technology (e.g., listserv, email, social networking applications) instead of face to face.

Public Conversations: are those that are open and accessible to anyone. Conversations can occur between many individuals behind closed walls, e.g., a major company’s employees working on a major project.

Open Coding: involves reading and comparing individual data units so as to label similar units into categories.

Networked Communities: are those support network-based communities that have few, if any, geographic and/or temporal boundaries, which VLSCs support.

Group-Level Cohesion: can be used to identify who was communicating with whom in a discussion forum.

Sociograms: visually convey relationships between actors. These sociograms make network structure explicit as collections of nodes with links that portray directionality and connection strength.

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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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