TY - GEN
T1 - A framework for longitudinal influence measurement between communication content and social networks
AU - Wang, Shenghui
AU - Groth, Paul
PY - 2011
Y1 - 2011
N2 - Artificial intelligence has a long history of learning from domain problems ranging from chess to jeopardy. In this work, we look at a problem stemming from social science, namely, how do social relationships influence communication content and vice versa. The tools used to study communication content (content analysis) have rarely been combined with those used to study social relationships (social network analysis). Furthermore, there is even less work addressing the longitudinal characteristics of such a combination. This paper presents a general framework for measuring the dynamic bidirectional influence between communication content and social networks. The framework leverages the idea that knowledge about both kinds of networks can be represented using the same knowledge representation. In particular, through the use of Semantic Web standards, the extraction of networks is made easier. The framework is applied to two use-cases: online forum discussions and conference publications. The results provide a new perspective over the dynamics involving both social networks and communication content.
AB - Artificial intelligence has a long history of learning from domain problems ranging from chess to jeopardy. In this work, we look at a problem stemming from social science, namely, how do social relationships influence communication content and vice versa. The tools used to study communication content (content analysis) have rarely been combined with those used to study social relationships (social network analysis). Furthermore, there is even less work addressing the longitudinal characteristics of such a combination. This paper presents a general framework for measuring the dynamic bidirectional influence between communication content and social networks. The framework leverages the idea that knowledge about both kinds of networks can be represented using the same knowledge representation. In particular, through the use of Semantic Web standards, the extraction of networks is made easier. The framework is applied to two use-cases: online forum discussions and conference publications. The results provide a new perspective over the dynamics involving both social networks and communication content.
UR - http://www.scopus.com/inward/record.url?scp=84881032852&partnerID=8YFLogxK
U2 - 10.5591/978-1-57735-516-8/IJCAI11-459
DO - 10.5591/978-1-57735-516-8/IJCAI11-459
M3 - Contribución a la conferencia
AN - SCOPUS:84881032852
SN - 9781577355120
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2758
EP - 2763
BT - IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
T2 - 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Y2 - 16 July 2011 through 22 July 2011
ER -