RURLMAN: Matching Forum Users Across Platforms Using Their Posted URLs
Pages 484 - 491
Abstract
How can we leverage the URLs posted on online forums to connect forum users with their profiles on other platforms? Most previous studies primarily focus on analyzing textual content and user metadata, paying limited attention to URLs. In this paper, we propose RURLMAN, a modular ensemble of methods for leveraging user-posted URLs to connect online forum users with their cross-platform profiles. Our approach has two key features: (a) we focus on user-posted URLs as the key source of information, and (b) we utilize a modular stacked ensemble integrating multiple methods, including string-matching and two ChatGPT capabilities. We show that RURLMAN effectively combines the strengths of its component methods, outperforming each individual method with an F1 score of 92.6%. We apply RURLMAN in a case study comprising 1.3M URLs posted by 250K forum users across six online security forums and consider URLs to Twitter, Facebook, GitHub, and YouTube. First, we match 30% of the users who shared URLs to these platforms with the corresponding owners of the linked social media profiles. Second, we connect 8% of these users to profiles on multiple platforms. Finally, we identify and analyze "groups" of users based on their posted URLs. To facilitate further research, we will share access to RURLMAN and its datasets with the research community.
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Published: 15 March 2024
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ASONAM '23
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ASONAM '23: International Conference on Advances in Social Networks Analysis and Mining
November 6 - 9, 2023
Kusadasi, Turkiye
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ASONAM '23 Paper Acceptance Rate 53 of 145 submissions, 37%;
Overall Acceptance Rate 116 of 549 submissions, 21%
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