Semantic Analysis Of Gatekeepers In Collaborative E‐learning Environments.Stuetzer, Cathleen M.; Carley, Kathleen M.; Koehler, Thomas; Thiem, Gerhard; Diesner, JanaWords and <strong>Network</strong>s ‐ Roles, Health, MethodsCommunication <strong>Network</strong>s, Communities Of Practice, Semantic <strong>Network</strong>s, Role Theory, Learning Commmunities, GatekeepersFRI.PM1The analysis of social roles in social structures has a long tradition in the social sciences. Especially the structural positions of social actors are crucial <strong>for</strong>studying processes of innovation and diffusion. Prior research has shown that in digital knowledge networks, gatekeepers acquire crucial positions by <strong>for</strong>mingdynamic chains of flow in the process of knowledge transfer. There<strong>for</strong>e it is important to be able to detect and describe the function of gatekeepers <strong>for</strong> digitallearning processes. We present a case study in which we explore the role of gatekeepers in <strong>for</strong>mal and in<strong>for</strong>mal online learning communities through acombination of social network analysis and relational text analysis. Our data come from e‐learning discussion boards that are actively used by elevenuniversities located in the state of Saxony, Germany. In order to examine the flow of in<strong>for</strong>mation through the network of learners and educators and to analyzethe network data, we use the network text analysis tool AutoMap and the network analysis tool ORA. We extract semantic networks from the data, andcombine them with social network data. By per<strong>for</strong>ming structural analysis on the resulting relational data we identify the roles of gatekeepers in this e‐learningenvironment as well as the relationship between semantic network structure and in<strong>for</strong>mation flow processes. With this research we ultimately aim tocontribute to a better understanding of the relationship between theories about socio‐technical networks, communication, and human learning.Sequencing In In<strong>for</strong>mation Dissemination In Scholarly <strong>Network</strong>sMo, Guang Ying; Dimitrova, Dimitrina; Gruzd, Anatoliy; Hayat, Zack; Mok, Diana; Wellman, BarryAcademic and Scientific <strong>Network</strong>sStructure, media use, in<strong>for</strong>mation dissemination, SequenceFRI.AM2This study investigates social networks from an original perspective of sequence, which is an important dimension of social structuring time. Althoughin<strong>for</strong>mation and communication technologies (ICTs) enable in<strong>for</strong>mation dissemination to numerous receivers simultaneously, people do not necessarily sendout messages to all their contacts at the same time. Instead, they choose different media at different times to pass the in<strong>for</strong>mation to different people. Thesequence of the contacts is a result of the actors’ process of determining on the primary and secondary contacts within social networks. This study investigateshow hierarchical structure in social networks influence actor’s sequencing of in<strong>for</strong>mation dissemination in scholarly networks. To answer this question, I studyin<strong>for</strong>mation dissemination in scholarly networks within the Graphics, Animation and New Media (GRAND) <strong>Network</strong> of Centres of Excellence. Interviews wereconducted with 30 professors in GRAND to understand their sequences in providing in<strong>for</strong>mation within their projects. To find the relationship between socialstructure and sequencing process, I asked interviewees how they contact collaborators under three scenarios: important / non‐urgent, urgent, and routine.Preliminary findings show (1) the stronger the strength of ties, the more prioritized they are in the sequence; (2) <strong>for</strong>mal position in the networks is related toawareness of sequencing; (3) the awareness of network members’ <strong>for</strong>mal positions and personal preference is related to the development of norms ofsequencing. Providing a new perspective <strong>for</strong> social science research, the findings of this study contribute to a further understanding of the <strong>for</strong>mation of socialnorms in social networks.
Sex And Drug HIV‐risk <strong>Network</strong>s Among Latino Migrant Men In A New Receiving EnvironmentKissinger, Patricia ; Friedman, Samuel R.; Muth, Stephen Q.; Schmidt, Norine; Anderson‐Smits, Colin; Shedlin, Michele<strong>Social</strong> <strong>Network</strong>s and HealthHIV/STD, Sex <strong>Network</strong>s, Drug UseSAT.AM1Introduction: In 2005, Hurricane Katrina led to an influx of Latino migrant men (LMM) to New Orleans to work in reconstruction. An urban environment withhigh rates of drug use, HIV, and other sexually transmitted diseases (STDs), New Orleans had a relatively small Latino population prior to Katrina. Marginalizedby legal status, poverty, low education, and lack of English language proficiency, LMM are more vulnerable to diseases and drug use. In new receivingenvironments, vulnerabilities may be magnified. Our prior work revealed that isolation from sexual partners/families led some men to have, and sometimes toshare, multiple short‐term sex partners, including female sex workers which could increase their vulnerability to HIV/STDs. The influence of risk networks onLMM risk and resilience <strong>for</strong> HIV/STDs is the focus of this study. Methods: Respondent‐driven sampling with steering incentives produced a cohort of 25 drugusing and 25 non‐drug using LMM egos. Participants will be given monetary incentives to recruit 2 generations of alters with an attempt to uniquely‐identifyall persons and links between them. Pajek and MS Access will be integrated to facilitate data capture of participant‐aided sociograms. Sociometric data,merged with computer‐assisted personal interviews, will be analyzed to: 1) determine feasibility of conducting sociometric analysis with LMM; 2) determinenetwork predictors, particularly k‐plex rank, of HIV/STD risk and morbidity and 3) explore the relative contribution of network predictors beyond individual,cultural and other environmental factors on these outcomes. Results: Data collection will start in December 2010. Preliminary findings will be presented alongwith lessons learned.Similarity Feeds Connection, Diversity Spices It: Exploring The Structure Of Advice <strong>Network</strong>s In A Knowledge <strong>Network</strong>Koku, Emmanuel F.; Dimitrova, DimaEducation, knowledge and learning networksKnowledge <strong>Network</strong>s, Advice <strong>Network</strong>, Communities Of Practice, Collaboration <strong>Network</strong>, scholarly networksSUN.AM1Increasingly, collaborative learning has <strong>for</strong>med the cornerstone of knowledge transfer and innovation in a variety of social arenas, from scientific research tobusiness, education, or health. To meet the challenge of knowledge transfer and innovation, the Canadian government created the <strong>Network</strong>s of Centres ofExcellence (NCE) program, which fosters research and innovation in partnerships with industrial and government participants. Such trends lead to theproliferation of organizational <strong>for</strong>ms that have to grapple with several challenges, including how to share knowledge among geographically dispersedcommunities of professionals. Advances in technology provide the infrastructure <strong>for</strong> contact, though they provide only a partial answer. <strong>Social</strong> network analysisemphasizes the role of in<strong>for</strong>mal network connections in channeling access to in<strong>for</strong>mation and advice. However, there is paucity of data on how contextualvariables (such as managerial practices or <strong>for</strong>mal policies) interact with other structural dynamics in shaping the flows of advice and in<strong>for</strong>mation exchangenetworks in knowledge networks. The presentation will use findings from the social network surveys of a Canadian NCE, supplemented with insights from andinterviews and documentary data, to explore how in<strong>for</strong>mal communication networks, managerial practices and governmental policy initiatives shape thestructure and content of advice network exchanges in a knowledge network.
- Page 1:
Sunbelt XXXIInternational Network f
- Page 5 and 6:
A Mixed‐method Approach To Subgro
- Page 7 and 8:
A Novel Hybrid Egocentric‐archiva
- Page 9 and 10:
A Social Network Analysis Of Tsiman
- Page 11 and 12:
A Technique For Analyzing Ergm Beha
- Page 13 and 14:
Actor Heterogeneity In Dynamic Infl
- Page 15 and 16:
An Agent‐based Approach To Evalua
- Page 17 and 18:
Anxious Solitude And Social Disinte
- Page 19 and 20:
Are You Getting What You Came For?
- Page 21 and 22:
Association Of Social Networks, Psy
- Page 23 and 24:
Birds Of The Feather Flock Together
- Page 25 and 26:
Building Political Echo Chambers: H
- Page 27 and 28:
Centrality, Structural Holes And St
- Page 29 and 30:
Coalitions In Exchange Networks: So
- Page 31 and 32:
Collaborating Through Networks: The
- Page 33 and 34:
Collaboration For Collective Action
- Page 35 and 36:
Collaborators Or Friends: Longitudi
- Page 37 and 38:
Comparing Local Configurations In S
- Page 39:
Cooperation Dynamics In Networks Cu
- Page 42:
Cross‐sectional Approximations To
- Page 45 and 46:
Designing Policy Tools For Building
- Page 47 and 48:
Discovering Jewish NetworksKadushin
- Page 49 and 50:
Dyadic Reciprocity And The Emergenc
- Page 51 and 52:
Dynamics Of Scientific Collaboratio
- Page 53 and 54:
Efficient Structures For Innovative
- Page 55 and 56:
Entrepreneurship And Local Embedded
- Page 57 and 58:
Exploring Knowledge Transfer In Vir
- Page 59 and 60:
Extracting Leadership And Influence
- Page 61 and 62:
Failing To See Or Failing To Seize
- Page 63 and 64:
Flows Of Cultural Consecration: Who
- Page 65 and 66:
Fostering Support For Innovative Pr
- Page 67 and 68:
From Whitehall To Leadenhall: Busin
- Page 69 and 70:
Game Modeling In Public Organizatio
- Page 71 and 72:
Generosity As A Public Good: Hetero
- Page 73 and 74:
Harvard Catalyst Profiles: An Open
- Page 75 and 76:
Homophily And Propinquity In Social
- Page 77 and 78:
How Far Our Network Perceptions Are
- Page 79 and 80:
Impact Of Team Faultlines On Socio
- Page 81 and 82:
Individualization As Driving Force
- Page 83 and 84:
Influences Of Return Migration On I
- Page 85 and 86:
Innovation Through ImitationGladsto
- Page 87 and 88:
Interplay Between Individual And Gr
- Page 89 and 90:
Is It Just Me, Or Are You Stressed?
- Page 91 and 92:
Knowledge Sharing In Non‐knowledg
- Page 93 and 94:
Link‐trace Sampling For Social Ne
- Page 95 and 96: Lotico The Semantic Social NetworkN
- Page 97 and 98: Mapping Networks: Spatial Visualiza
- Page 99 and 100: Markets Or Networks: Rural Househol
- Page 101 and 102: Measurement Error In Network Analys
- Page 103 and 104: Micro‐mobilization In A Scientifi
- Page 105 and 106: Mixing In Large Populations: Some N
- Page 107 and 108: Modeling Real World Interpersonal N
- Page 109 and 110: Multilevel Longitudinal Analysis Fo
- Page 111 and 112: Multiplexity, Heterogeneity And Ove
- Page 113 and 114: Network Centralization And The Diss
- Page 115 and 116: Network Influence On Adolescent Alc
- Page 117 and 118: Network Structure And A Proposed
- Page 119 and 120: Networked Play For Health: Promotin
- Page 121 and 122: Networks, Collective Action, And St
- Page 123 and 124: Online And Offline Ego‐centered N
- Page 125 and 126: Organizational Embeddedness And Str
- Page 127 and 128: Peer Effects In Migrants’ Remitta
- Page 129 and 130: Personal Influence Among Core Ties
- Page 131 and 132: Perspectives To Soldiers' Professio
- Page 133 and 134: Predicting Survival From Social Net
- Page 135 and 136: R&D Networks, Knowledge Transfer An
- Page 137 and 138: Reasoning About Large‐scale Socia
- Page 139 and 140: Relational Compensation In Entrepre
- Page 141 and 142: Representation Of Complex Cognitive
- Page 143 and 144: Road Networks And Insurgent Violenc
- Page 145: School/community Policing‐‐mixe
- Page 149 and 150: Small EU Member States' Export Patt
- Page 151 and 152: Social Constraints, Agency, And Ins
- Page 153 and 154: Social Network Analysis In Multiple
- Page 155 and 156: Social Network Resilience Given Inf
- Page 157 and 158: Social Networks And Sustainability
- Page 159 and 160: Social Snacking: Friends’ Influen
- Page 161 and 162: Static Visualization Of Network Dyn
- Page 163 and 164: Structure And Consistency: Assessme
- Page 165 and 166: Teaching Undergraduates Social Netw
- Page 167 and 168: The Development Of Japanese Interlo
- Page 169 and 170: The Effects Of Social Networks, Pro
- Page 171 and 172: The Formation Of Social Capital And
- Page 173 and 174: The Impact Of Different Non‐respo
- Page 175 and 176: The Influential Power Of eWOM Distr
- Page 177 and 178: The Paradox Of Connection: Social N
- Page 179 and 180: The Return Of Quality Of Social Cap
- Page 181 and 182: The Small World Phenomena Of Patent
- Page 183 and 184: The Structure Of Consensus: Cohesio
- Page 185 and 186: Time Heterogeneity In Stochastic Ac
- Page 187 and 188: Two‐mode Projection And Data Loss
- Page 189 and 190: Understanding The Different Facets
- Page 191 and 192: Using A Web‐based Survey To Elici
- Page 193 and 194: Using Social Network Analysis To Ma
- Page 195 and 196: Using Weak‐ties For Problem Solvi
- Page 197 and 198:
Viable And Non‐viable Models Of C
- Page 199 and 200:
Voting Power CentralityCarnegie, Je
- Page 201 and 202:
Who Shares? Exploration Of Manageri
- Page 203 and 204:
Worlds Apart ‐ Network Emergence
- Page 205 and 206:
Aupetit, Michael ‐ CEA LIST ‐ m
- Page 207 and 208:
Borgatti, Steve ‐ U of Kentucky,
- Page 209 and 210:
Chiang, Yen‐sheng ‐ University
- Page 211 and 212:
De salabert, Arturo ‐ Universidad
- Page 213 and 214:
Everett, Martin ‐ University of M
- Page 215 and 216:
Garayev, Vener ‐ University of Ce
- Page 217 and 218:
Hahn, Christian stadil ‐ Enso Con
- Page 219 and 220:
Houtman, Leonie ‐ VU University,
- Page 221 and 222:
Kaminski, Jermain ‐ Massachusetts
- Page 223 and 224:
La pointe, Allison ‐ Minnesota De
- Page 225 and 226:
Luque, John ‐ Georgia Southern Un
- Page 227 and 228:
Mo, Guang ying ‐ University of To
- Page 229 and 230:
Oh, Yoon ‐ USC ‐ yoonkyuo@usc.e
- Page 231 and 232:
Portales, Luis ‐ Tecnológico de
- Page 233 and 234:
Samila, Sampsa ‐ Brock University
- Page 235 and 236:
Shoham, David ‐ Loyola University
- Page 237 and 238:
Strully, Kate ‐ University at Alb
- Page 239 and 240:
Valente, Annie ‐ UC‐San Francis
- Page 241 and 242:
Welch, Eric ‐ University of Illin
- Page 243:
Zhu, Jonathan ‐ City University o