Devan Rosen
University of Hawaii
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Publication
Featured researches published by Devan Rosen.
New Media & Society | 2011
Devan Rosen; Pascale Roy Lafontaine; Blake Hendrickson
The current study investigates engagement activities in an online resource exchange community exploring elements such as sense of belonging, connectedness, and trust. CouchSurfing.com is an online cultural exchange community in which members from around the globe coordinate travel accommodations and organize gatherings with fellow members via a social media platform. Findings confirmed that members who have not met face-to-face with other members have a lower sense of belonging to the community than those who have. Increased attendance to gatherings was positively related to sense of belonging to the community, and hosting had a positive relationship with trust in the community. Additionally, CouchSurfers reported that they preferred to be contacted through personal e-mails rather then group e-mails, while those who reported an increased participation in gatherings found group e-mails to be useful.
Social Network Analysis and Mining | 2011
Devan Rosen; George A. Barnett; Jang Hyun Kim
The science of social network analysis has co-evolved with the development of online environments and computer-mediated communication. Unique and precise data available from computer and information systems have allowed network scientists to explore novel social phenomena and develop new methods. Additionally, advances in the structural analysis and visualization of computer-mediated social networks have informed developers and shaped the design of social media tools. This article reviews some examples of research that highlight the ways that social network analysis has evolved with online data. Examples include the international hyperlink network, political blogs and hyperlinks, social media, and multi-user virtual environments. The data available from online environments makes several important contributions to network science, including reliable network flow data, unique forms of relational data across a myriad of contexts, and dynamic data allowing for longitudinal analysis and the animation of social networks.
learning analytics and knowledge | 2011
Daniel D. Suthers; Devan Rosen
Learning and knowledge creation is often distributed across multiple media and sites in networked environments. Traces of such activity may be fragmented across multiple logs and may not match analytic needs. As a result, the coherence of distributed interaction and emergent phenomena are analytically cloaked. Understanding distributed learning and knowledge creation requires multi-level analysis of the situated accomplishments of individuals and small groups and of how this local activity gives rise to larger phenomena in a network. We have developed an abstract transcript representation that provides a unified analytic artifact of distributed activity, and an analytic hierarchy that supports multiple levels of analysis. Log files are abstracted to directed graphs that record observed relationships (contingencies) between events, which may be interpreted as evidence of interaction and other influences between actors. Contingency graphs are further abstracted to two-mode directed graphs that record how associations between actors are mediated by digital artifacts and summarize sequential patterns of interaction. Transitive closure of these associograms creates sociograms, to which existing network analytic techniques may be applied, yielding aggregate results that can then be interpreted by reference to the other levels of analysis. We discuss how the analytic hierarchy bridges between levels of analysis and theory.
hawaii international conference on system sciences | 2010
Devan Rosen; Michael A. Stefanone; Derek Lackaff
Research shows that people from different cultural backgrounds and gender roles behave and communicate in systematically different ways. The current research utilized a survey (N=452) of young adults to examine the occurrence of culturally- and gender-influenced differences in online behavior, offline networks, and satisfaction. Results show that participants who identify with more individualistic cultural backgrounds have larger networks of friends on social network sites (SNSs), have a greater proportion of these friends not actually met face-to-face, and share more photos online opposed to participants who identify with less individualistic cultural backgrounds. Social support network size was a significant predictor of satisfaction with life, while SNS network size was not. Findings suggest that participants who identify with more individualistic cultural backgrounds tend to self-promote and are better connected and more satisfied with their social lives. It seems offline networks are more important than mediated networks in terms of psychological well-being.
Journal of Computer-Mediated Communication | 2006
Devan Rosen; Joseph Woelfel; Dean Krikorian; George A. Barnett
This article details a set of procedures for the analysis and interpretation of the content and structure of online networks and communities. These novel methods allow for the analysis of online chat, including parsing the data into separate and interrelated files to determine individual, group and organizational patterns. An illustrative example of an educational online community in Active Worlds Educational Universe (AWEDU) is provided that uses three-dimensional virtual worlds for student interaction. Findings from semantic network analysis procedures reveal elements of the online interaction that would otherwise be difficult to extract given the great amount of textual data produced in such communities. The case study allows for qualitative and quantitative analyses. The limitations of the procedures are discussed along with planned developments and their social implications.
learning analytics and knowledge | 2011
Devan Rosen; Victor Miagkikh; Daniel D. Suthers
Multi-user virtual environments (MUVEs) allow many users to explore the environment and interact with other users as they learn new content and share their knowledge with others. The semi-synchronous communicative interaction within these learning environments is typically text-based Internet relay chat (IRC). IRC data is stored in the form of chatlogs and can generate a large volume of data, posing a difficulty for researchers looking to evaluate learning in the interaction by analyzing and interpreting the patterns of communication structure and related content. This paper describes procedures for the measurement and visualization of chat-based communicative interaction in MUVEs. Methods are offered for structural analysis via social networks, and content analysis via semantic networks. Measuring and visualizing social and semantic networks allows for a window into the structure of learning communities, and also provides for a large cache of analytics to explore individual learning outcomes and group interaction in any virtual interaction. A case study on a learning based MUVE, SRIs Tapped-In community, is used to elaborate analytic methods.
hawaii international conference on system sciences | 2011
Devan Rosen; Daniel D. Suthers
Social network analysis is primarily based in the investigation of ties between nodes and the groups that those ties form. Computer-mediated interaction has introduced many unique forms of tie data to the field. The form of data used in this research are traces of activity left when people create and edit digital artifacts, and when navigating around hyperlinked environments. Using intentional and unintentional traces of activity to generate social graphs provides a unique window into collaboration and interaction. This paper elaborates a technique that uses event log data to trace contingencies in user activity and generate directed two-mode graphs, associograms, which can then be abstracted to sociogram representations. The social network ties generated represent connections between people based on actions contingent on one another. These ties can be used to represent potential social connections for collaboration, social collectives for coordination, and stigmergic self-organization.
hawaii international conference on system sciences | 2011
Devan Rosen; Kar-Hai Chu
Tie measurement is an analytic foundation of social network analysis, and has been most commonly measured in regards to the strength of the tie. The strong tie - weak tie dichotomy is conceptually misleading, and the science of networks lacks a clear conceptualization of ties that allows for consistent operationalization of the concept. This conceptual gap has led researchers to measure ties in a contextually specific manner, often unique to their particular research design. This paper offers a reconceptualization of the tie concept as the utility of a tie, and provides a multi-dimensional taxonomy of social network ties that can be used to assess appropriate tie measures. Conceptual dimensions include socio-emotional closeness, resource potential, and accessibility. The taxonomy allows researchers to fit their specific operational needs within a common conceptual framework of tie measures. Visual representations are offered elaborating and exploring the implications of the proposed reconceptualization.
hawaii international conference on system sciences | 2016
Devan Rosen; George A. Barnett
Introduction to the Network Analysis of Digital and Social Media Minitrack.
hawaii international conference on system sciences | 2012
Devan Rosen; George A. Barnett
Introduction to the Communication and Social Networks Minitrack