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The development of social media user stance detection and bot detectionmethods rely heavily on large-scale and high-quality benchmarks. However, inaddition to low annotation quality, existing benchmarks generally haveincomplete user relationships, suppressing graph-based account detectionresearch. To address these issues, we propose a Multi-Relational Graph-BasedTwitter Account Detection Benchmark (MGTAB), the first standardized graph-basedbenchmark for account detection. To our knowledge, MGTAB was built based on thelargest original data in the field, with over 1.55 million users and 130million tweets. MGTAB contains 10,199 expert-annotated users and 7 types ofrelationships, ensuring high-quality annotation and diversified relations. InMGTAB, we extracted the 20 user property features with the greatest informationgain and user tweet features as the user features. In addition, we performed athorough evaluation of MGTAB and other public datasets. Our experiments foundthat graph-based approaches are generally more effective than feature-basedapproaches and perform better when introducing multiple relations. By analyzingexperiment results, we identify effective approaches for account detection andprovide potential future research directions in this field. Our benchmark andstandardized evaluation procedures are freely available at:https://github.com/GraphDetec/MGTAB.

MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark