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
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.
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:
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.
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).
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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