TY - GEN
T1 - A methodology for integrating network theory and topic modeling and its application to innovation diffusion
AU - Diesner, Jana
AU - Carley, Kathleen M.
PY - 2010/11/29
Y1 - 2010/11/29
N2 - Text data pertaining to socio-technical networks often are analyzed separately from relational data, or are reduced to the fact and strength of the flow of information between nodes. Disregarding the content of text data for network analysis can limit our understanding of the effects of language use in networks. We present a computational and interdisciplinary methodology that addresses this limitation by combining theory from socio-linguistics with social network analysis and machine learning based text mining: we use network analysis to identify groups of individuals who assume the theoretically grounded roles of change agents and preservation agents. People in these roles differ in their motivation and capability to induce and adopt change in a network. Topic modeling is then constrained to the texts authored by people in these roles. We apply this methodology to a public dataset of about 55,000 research proposals that were granted funding. Our results suggest that the people per role differ in the research domains they work on and the strength of association with those domains that both roles are involved with, but are similar with respect to fulfilling the task or additional role of being a project manager.
AB - Text data pertaining to socio-technical networks often are analyzed separately from relational data, or are reduced to the fact and strength of the flow of information between nodes. Disregarding the content of text data for network analysis can limit our understanding of the effects of language use in networks. We present a computational and interdisciplinary methodology that addresses this limitation by combining theory from socio-linguistics with social network analysis and machine learning based text mining: we use network analysis to identify groups of individuals who assume the theoretically grounded roles of change agents and preservation agents. People in these roles differ in their motivation and capability to induce and adopt change in a network. Topic modeling is then constrained to the texts authored by people in these roles. We apply this methodology to a public dataset of about 55,000 research proposals that were granted funding. Our results suggest that the people per role differ in the research domains they work on and the strength of association with those domains that both roles are involved with, but are similar with respect to fulfilling the task or additional role of being a project manager.
KW - Research funding
KW - Socio-technical networks
KW - Topic modeling
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=78649261844&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649261844&partnerID=8YFLogxK
U2 - 10.1109/SocialCom.2010.106
DO - 10.1109/SocialCom.2010.106
M3 - Conference contribution
AN - SCOPUS:78649261844
SN - 9780769542119
T3 - Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust
SP - 687
EP - 692
BT - Proceedings - SocialCom 2010
T2 - 2nd IEEE International Conference on Social Computing, SocialCom 2010, 2nd IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2010
Y2 - 20 August 2010 through 22 August 2010
ER -