Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence |
| Pages | 2758-2763 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 1 Dec 2011 |
| Externally published | Yes |
| Event | 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Spain Duration: 16 Jul 2011 → 22 Jul 2011 Conference number: 22 |
Conference
| Conference | 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 |
|---|---|
| Abbreviated title | IJCAI |
| Country | Spain |
| City | Barcelona |
| Period | 16/07/11 → 22/07/11 |
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A framework for longitudinal influence measurement between communication content and social networks. / Wang, Shenghui; Groth, Paul.
IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence. 2011. p. 2758-2763.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - A framework for longitudinal influence measurement between communication content and social networks
AU - Wang, Shenghui
AU - Groth, Paul
PY - 2011/12/1
Y1 - 2011/12/1
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 - Conference contribution
SN - 9781577355120
SP - 2758
EP - 2763
BT - IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
ER -