A framework for longitudinal influence measurement between communication content and social networks

Shenghui Wang, Paul Groth

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)
2 Downloads (Pure)

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 languageEnglish
Title of host publicationIJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
Pages2758-2763
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Spain
Duration: 16 Jul 201122 Jul 2011
Conference number: 22

Conference

Conference22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Abbreviated titleIJCAI
CountrySpain
CityBarcelona
Period16/07/1122/07/11

Fingerprint Dive into the research topics of 'A framework for longitudinal influence measurement between communication content and social networks'. Together they form a unique fingerprint.

  • Cite this

    Wang, S., & Groth, P. (2011). A framework for longitudinal influence measurement between communication content and social networks. In IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence (pp. 2758-2763) https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-459