TY - GEN
T1 - Graph Learning of Multifaceted Motivations for Online Engagement Prediction in Counter-party Social Networks
AU - Hu, Manting
AU - Lin, Qingyuan
AU - Zhang, Denghui
AU - Lu, Angela
AU - Liu, Junming
AU - Xiong, Hui
N1 - Publisher Copyright:
© 2023 International Conference on Information Systems, ICIS 2023: "Rising like a Phoenix: Emerging from the Pandemic and Reshaping Hu. All Rights Reserved.
PY - 2023
Y1 - 2023
N2 - Social media has emerged as an essential venue to invigorate online political engagement. However, political engagement is multifaceted and impacted by both individuals’ self-motivation and social influence from peers and remains challenging to model in a counter-party network. Therefore, we propose a counter-party graph representation learning model to study individuals’ intrinsic and extrinsic motivations for online political engagement. Firstly, we capture users’ intrinsic political interests providing self-motivation from a user-topic network. Then, we encode how users cast influence on others from the inner-/counter-party through a user-user network. With the learned embedding of intrinsic and extrinsic motivations, we model the interactions between these two facets and utilize the dependency by deep sequential model decoding. Finally, extensive experiments using Twitter data related to the 2020 U.S. presidential election and the 2019 HK protests validate the model’s predictive power. This study has implications for online political engagement, political participation, and political polarization.
AB - Social media has emerged as an essential venue to invigorate online political engagement. However, political engagement is multifaceted and impacted by both individuals’ self-motivation and social influence from peers and remains challenging to model in a counter-party network. Therefore, we propose a counter-party graph representation learning model to study individuals’ intrinsic and extrinsic motivations for online political engagement. Firstly, we capture users’ intrinsic political interests providing self-motivation from a user-topic network. Then, we encode how users cast influence on others from the inner-/counter-party through a user-user network. With the learned embedding of intrinsic and extrinsic motivations, we model the interactions between these two facets and utilize the dependency by deep sequential model decoding. Finally, extensive experiments using Twitter data related to the 2020 U.S. presidential election and the 2019 HK protests validate the model’s predictive power. This study has implications for online political engagement, political participation, and political polarization.
KW - Graph Learning
KW - Multifaceted Graph
KW - Online Political Engagement
KW - Social Network
UR - http://www.scopus.com/inward/record.url?scp=85192513038&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85192513038&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85192513038
T3 - International Conference on Information Systems, ICIS 2023: "Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies"
BT - International Conference on Information Systems, ICIS 2023
T2 - 44th International Conference on Information Systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023
Y2 - 10 December 2023 through 13 December 2023
ER -