Sunbelt XXXI International Network for Social Network ... - INSNA

Using A Web‐based Survey To Elicit Inter‐organizational Network Information: Findings From The Community Partners In Care Study Administrator SurveysStockdale, Susan E.; Horta, Mariana; Mendel, Peter J.; Jones, Felica; Dixon, Elizabeth; Chung, BowenCollecting Network DataMental Health Services, Inter‐organizational Networks, Survey, Community Based Participatory ResearchTHURS.PM1In‐person interviews are the usual method for collecting information on social networks, but conducting the interviews and coding the data is time‐consumingand costly. In the Community Partners in Care (CPIC) study, we designed and fielded a web‐based survey to elicit data on inter‐organizational networks fromcommunity agency and program administrators. We ran 2 versions of the survey to explore reliability and data quality issues associated with different ways ofeliciting network information: asking respondents to list partners and then describe attributes of partnerships vs. asking them to describe attributes ofpartnerships as each partner is nominated. In particular, we were interested in whether the “listing first” method would result in ego‐centric networks withlarger outdegrees, as has been found with in‐person network interviews, and whether the organization names respondents typed into open‐ended list itemswould be of sufficient quality to identify existing community agencies. We also explore whether controlling for administrator/agency characteristics explainsthe reliability and quality differences we observed between the 2 web‐survey versions. We found that web‐based surveys yielded data with comparable qualityand reliability as compared with in‐person network interviews, but more time and follow‐up than had been anticipated was required to obtain a good responserate and ensure high quality data. We discuss challenges we experienced with web‐based surveys, including software “bugs”, uncertainty regarding theorganizational unit respondents provided information for, and the inability to verify (through interviewer follow‐up probes and questions) partner organizationand program names and other information elicited from the network questions.Using Dialog For Community Detection In Virtual WorldsShah, Fahad; Sukthankar, GitaOnline Social NetworksSocial Network, Virtual World, Community DetectionTHURS.PM1Massively multi‐player online games and virtual environments provide new outlets for human social interaction that differ from both face‐to‐face interactionsand non‐physically‐embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting andanalyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, andanalyzing unstructured conversations between changing groups of participants in Second Life, a massively multi‐player online user‐constructed environmentthat allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructuredchat data alone is a difficult problem. Linkages between SL actors are inferred offline by partitioning the unstructured data into separate conversations; bycombining spatio‐temporal cues with a semantic analysis of the dialogs in public chat data, we can construct an approximate social network of the users fromour dataset of 80,183 separate utterances. In this study, we evaluate different partitioning algorithms for dividing the network into communities anddemonstrate how knowledge of the community structure can be used to refine the semantic labeling of the dialogs.