Social network analysis in virtual environments

Abstract

Multi-user virtual environments (MUVEs) allow users to navigate and explore the environment as well as interact with other users. The interaction within these environments is often text-based using Internet relay chat (IRC) and related systems. IRC poses a difficulty for researchers looking to analyze and interpret the communicative interaction since data is stored in the form of chatlogs. The current research proposes and applies methodological procedures for the representation and analysis of interaction in MUVEs as social networks. A case study on SciCentr programs from Cornell University is used to elaborate methods and related findings.

Applications of Cohesive Subgraph Detection Algorithms to Analyzing Socio-Technical Networks

“…(These numbers are clearly specific to the dataset, as different populations carrying out different tasks would vary in rate of interaction. For example, [19] found a 30 second window to be sufficient for analyzing student IRC chats.) But longer windows incur greater computational cost since there are many more contingencies in the graph.…”

Identifying Uptake, Sessions, and Key Actors in a Socio-technical Network

“…Educator chats tend to be deliberative; hence, for our data we set the window for the Proximal Event contingency at 120 seconds before the contribution (Table 1). Shorter durations have been used for faster paced chats [15]. We are initially using conservative parameters for Keystroke Level Modeling, namely expert typist values published in [13] of M=0.6 and K=0.08, where M the average time for a mental operation initiating the action and K is the average time to strike a key.…”

Social networks and online environments: when science and practice co-evolve

“…However, the methods developed were microanalytic and have yet to be employed on large-scale data. Rosen et al (2003) explicated a methodology for semantic network analyses of IRC interaction in MUVEs, representing a methodological advancement in the quantitative analysis of the content of IRC interaction. However, there had been little to no development of methods to extract social networks from IRC interaction until Rosen (2010) and Rosen and Corbit (2009) developed network analytic techniques for the measurement and representation of networks in IRC-based MUVEs.…”

“…Rosen et al (2003) explicated a methodology for semantic network analyses of IRC interaction in MUVEs, representing a methodological advancement in the quantitative analysis of the content of IRC interaction. However, there had been little to no development of methods to extract social networks from IRC interaction until Rosen (2010) and Rosen and Corbit (2009) developed network analytic techniques for the measurement and representation of networks in IRC-based MUVEs. Many of these techniques map, display, and study thread-based online communities, such as Usenet groups; whereas graphical chat rooms sequentially log chat interaction, which is difficult to separate and analyze as sub-groups, parsed interaction, or as a structural system.…”

“…Many of these techniques map, display, and study thread-based online communities, such as Usenet groups; whereas graphical chat rooms sequentially log chat interaction, which is difficult to separate and analyze as sub-groups, parsed interaction, or as a structural system. Even though Rosen et al (2003) analyzed the content of IRC interaction in MUVWs using semantic network analysis, there still remained a gap in procedures to extract structural social networks from IRC. Many of the parallel online community (e.g., Usenet) and social media (e.g., SNS) research streams have benefited from structural analysis and social network representation, but interaction via IRC is still one of the most common forms of interaction in a variety of contexts (i.e., online gaming, educational environments), yet the structure still remains cloaked behind the form of log file data used to store IRC.…”