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

Multilevel Longitudinal Analysis For Studying Influence Between Co‐evolving Social And Content NetworksWang, Shenghui; Groth, Paul; Kleinnijenhuis, Jan; Oegema, DirkAnalyzing Network DataSocial Network, Co‐evolution, Content Analysis, Longitudinal Analysis, InfluenceSAT.PM1The Social Semantic Web has begun to provide connections between users within social networks and the content they produce across the whole of the SocialWeb. Thus, the Social Semantic Web provides a basis to analyze both the communication behavior of users together with the content of their communication.However, there is little research combining the tools to study communication behaviour and communication content, namely, social network analysis andcontent analysis. Furthermore, there is even less work addressing the longitudinal characteristics of such a combination. This paper proposes to take intoaccount both the social networks and the communication content networks. We present a general framework for measuring the dynamic bi‐directionalinfluence between co‐evolving social and content networks. We focus on the twofold research question: how previous communication content and previousnetwork structure affect (1) the current communication content and (2) the current network structure. Multilevel time‐series regression models are used tomodel the influence between variables derived from social networks and content networks. The effects are studied at the group level as well as the level ofindividual actors. We apply this framework in two use‐cases: online forum discussions and conference publications. By analysing the dynamics involving bothsocial networks and content networks, we obtain a new perspective towards the connection of social behaviour in the social web and the traditional contentanalysis.Multilevel Models For Social Network And Group DependenciesTranmer, MarkAnalyzing Network DataStatistical Models, Network Autocorrelation Models, Multilevel ModelsSAT.PM1In social network analysis the network effects model and the network disturbances model are often used to allow for network dependencies. Another way toallow for dependencies in a population is via multilevel modelling. In this talk I propose some multilevel modelling approaches to allow for social networkdependencies. I begin by considering a single network, and then consider the situation where there are a number of groups, each of which contains a socialnetwork. An example of the second situation is a number of schools, each containing a friendship network for the pupils. In this example, the extent to whichan educational or behavioural outcome varies between pupils, social networks, and schools may be of substantive interest. I present some empirical results tocompare the multilevel models I propose with existing models for network dependencies. Such a comparison also allows an assessment of the effects ofignoring groups and/or social networks in statistical analysis.