TY - GEN
T1 - Towards a smart(er) social science using high-dimensional continuous-time trajectories from the open dynamic interaction networks (odin) platform
AU - Khan, Bilal
AU - Dombrowski, Kirk
AU - Bellam, Alekhya
AU - Sayeras, Gisela Font
AU - Pi, Kin
AU - Crawford, Devan
AU - Habecker, Patrick
AU - Jauernig, Maisha
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In this paper, we describe Open Dynamic Interaction Networks (ODIN), a software platform designed to move the social, behavioral, and public health sciences toward a new investigative paradigm. ODIN enables us to collect and analyze rich, contextual continuous-time data on both personal change and interpersonal interaction. It achieves this by supporting dynamic delivery of questions based on the currently sensed context of each participant. The ODIN system extends beyond static (or even stepwise dynamic) graph-theoretic renderings of social life and individual behavior by considering 'social relationship' to be measured in terms of high-dimensional continuous-time trajectories. The system is designed to be extensible, allowing seamless incorporation of new sensors, and correspondingly sophisticated compound rules by which contexts of interest may be specified. As such, ODIN opens the door for a 'smarter' social science based on continuous contextual data, and a 'smarter' data science that is reflective and sociologically informed.
AB - In this paper, we describe Open Dynamic Interaction Networks (ODIN), a software platform designed to move the social, behavioral, and public health sciences toward a new investigative paradigm. ODIN enables us to collect and analyze rich, contextual continuous-time data on both personal change and interpersonal interaction. It achieves this by supporting dynamic delivery of questions based on the currently sensed context of each participant. The ODIN system extends beyond static (or even stepwise dynamic) graph-theoretic renderings of social life and individual behavior by considering 'social relationship' to be measured in terms of high-dimensional continuous-time trajectories. The system is designed to be extensible, allowing seamless incorporation of new sensors, and correspondingly sophisticated compound rules by which contexts of interest may be specified. As such, ODIN opens the door for a 'smarter' social science based on continuous contextual data, and a 'smarter' data science that is reflective and sociologically informed.
KW - Adaptive survey
KW - Dynamic networks
KW - Ecological momentary assessment
KW - Longitudinal networks
KW - Social network analysis
KW - Temporal networks contextual survey
UR - http://www.scopus.com/inward/record.url?scp=85083555290&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083555290&partnerID=8YFLogxK
U2 - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00051
DO - 10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00051
M3 - Conference contribution
AN - SCOPUS:85083555290
T3 - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
SP - 37
EP - 44
BT - Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019
Y2 - 19 August 2019 through 23 August 2019
ER -