What if Social Bots Be My Friends? Estimating Causal Effect of Social Bots Using Counterfactual Graph Learning

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dc.contributor.author Wu, Ziyue
dc.contributor.author Zhang, Yiqun
dc.contributor.author Chen, Xi
dc.date.accessioned 2023-12-26T18:38:38Z
dc.date.available 2023-12-26T18:38:38Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other ca20575c-a3b5-4ff4-8c3e-823436f96b6e
dc.identifier.uri https://hdl.handle.net/10125/106684
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining, and Machine Learning for Social Media
dc.subject causal identification
dc.subject counterfactual graph learning
dc.subject homophily
dc.subject social bots
dc.title What if Social Bots Be My Friends? Estimating Causal Effect of Social Bots Using Counterfactual Graph Learning
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Social bots wield significant impact within social networks. Despite the widely recognized variations in individual responses to humans and bots, existing research has not thoroughly investigated the impact differences between human and social bots on individuals’ opinions. However, such differences are challenging to be estimated due to the presence of confounders introduced by homophily and the absence of counterfactual outcomes in observational network data. This study designs a counterfactual graph learning approach to accurately estimate causal effects, which exhibits superior performance in our simulations. The subsequent empirical results demonstrate that social bots yield a weaker influence than humans, and we further uncover diverse influential patterns of different types of opinions expressed by influence sources. Nevertheless, the impact difference is overestimated without applying our approach to control the confounders. Our research provides a practical approach and offers insights for stakeholders to scrutinize bots' impact from network perspectives.
dcterms.extent 10 pages
prism.startingpage 2485
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