What if Social Bots Be My Friends? Estimating Causal Effect of Social Bots Using Counterfactual Graph Learning
Files
Date
2024-01-03
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
2485
Ending Page
Alternative Title
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.
Description
Keywords
Data Analytics, Data Mining, and Machine Learning for Social Media, causal identification, counterfactual graph learning, homophily, social bots
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 57th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.