TY - GEN
T1 - What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding
AU - Kursuncu, Ugur
AU - Gaur, Manas
AU - Lokala, Usha
AU - Illendula, Anurag
AU - Thirunarayan, Krishnaprasad
AU - Daniulaityte, Raminta
AU - Sheth, Amit
AU - Arpinar, I. Budak
N1 - Funding Information:
ACKNOWLEDGEMENT Research reported in this publication was supported by National Institute on Drug Abuse (NIDA) of the National Institutes of Health (NIH) under award number 5R01DA039454-03. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/10
Y1 - 2019/1/10
N2 - With 93% of pro-marijuana population in US favoring legalization of medical marijuana, high expectations of a greater return for Marijuana stocks, and public actively sharing information about medical, recreational and business aspects related to marijuana, it is no surprise that marijuana culture is thriving on Twitter. After the legalization of marijuana for recreational and medical purposes in 29 states, there has been a dramatic increase in the volume of drug-related communications on Twitter. Specifically, Twitter accounts have been established for promotional and informational purposes, some prominent among them being American Ganja, Medical Marijuana Exchange, and Cannabis Now. Identification and characterization of different user types can allow us to conduct more fine-grained spatiotemporal analysis to identify dominant or emerging topics in the echo chambers of marijuana-related communities on Twitter. In this research, we mainly focus on classifying Twitter accounts created and run by ordinary users, retailers, and informed agencies. Classifying user accounts by type can enable better capturing and highlighting of aspects such as trending topics, business profiling of marijuana companies, and state-specific marijuana policymaking. Furthermore, type-based analysis can provide more profound understanding and reliable assessment of the implications of marijuana-related communications. We developed a comprehensive approach to classifying users by their types on Twitter through contextualization of their marijuana-related conversations. We accomplished this using compositional multiview embedding synthesized from People, Content, and Network views achieving 8% improvement over the empirical baseline.
AB - With 93% of pro-marijuana population in US favoring legalization of medical marijuana, high expectations of a greater return for Marijuana stocks, and public actively sharing information about medical, recreational and business aspects related to marijuana, it is no surprise that marijuana culture is thriving on Twitter. After the legalization of marijuana for recreational and medical purposes in 29 states, there has been a dramatic increase in the volume of drug-related communications on Twitter. Specifically, Twitter accounts have been established for promotional and informational purposes, some prominent among them being American Ganja, Medical Marijuana Exchange, and Cannabis Now. Identification and characterization of different user types can allow us to conduct more fine-grained spatiotemporal analysis to identify dominant or emerging topics in the echo chambers of marijuana-related communities on Twitter. In this research, we mainly focus on classifying Twitter accounts created and run by ordinary users, retailers, and informed agencies. Classifying user accounts by type can enable better capturing and highlighting of aspects such as trending topics, business profiling of marijuana companies, and state-specific marijuana policymaking. Furthermore, type-based analysis can provide more profound understanding and reliable assessment of the implications of marijuana-related communications. We developed a comprehensive approach to classifying users by their types on Twitter through contextualization of their marijuana-related conversations. We accomplished this using compositional multiview embedding synthesized from People, Content, and Network views achieving 8% improvement over the empirical baseline.
KW - Compositional Multiview Embedding
KW - Emoji Embedding
KW - Marijuana
KW - Network Embedding
KW - Semantic Social Computing
KW - User classification
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UR - http://www.scopus.com/inward/citedby.url?scp=85061938006&partnerID=8YFLogxK
U2 - 10.1109/WI.2018.00-50
DO - 10.1109/WI.2018.00-50
M3 - Conference contribution
AN - SCOPUS:85061938006
T3 - Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018
SP - 474
EP - 479
BT - Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018
Y2 - 3 December 2018 through 6 December 2018
ER -