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Read This Paper to Get $50 Million:* An Analysis of Mobile Messaging Scams Using Reddit Data

arXiv Security Archived May 19, 2026 ✓ Full text saved

arXiv:2605.16656v1 Announce Type: new Abstract: Mobile messaging scams--fraudulent messages delivered over SMS and other mobile applications--have become a persistent and evolving security threat, yet the attributes underlying these campaigns remain unclear. This study seeks to address this gap by examining trends in mobile messaging scams and testing the effectiveness of commercial and open-source off-the-shelf detection tools. We characterize mobile messaging scam operations, focusing on how p

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    Computer Science > Cryptography and Security [Submitted on 15 May 2026] Read This Paper to Get $50 Million:* An Analysis of Mobile Messaging Scams Using Reddit Data Allison Lu, Bernardo B. P. Medeiros, Kevin R. B. Butler, Patrick Traynor Mobile messaging scams--fraudulent messages delivered over SMS and other mobile applications--have become a persistent and evolving security threat, yet the attributes underlying these campaigns remain unclear. This study seeks to address this gap by examining trends in mobile messaging scams and testing the effectiveness of commercial and open-source off-the-shelf detection tools. We characterize mobile messaging scam operations, focusing on how phone numbers, URLs, and text content are used across campaigns. To achieve this objective, we collect and measure a dataset of 175,430 user-reported mobile messaging scams from Reddit between June 2020 and December 2025. While reply-based scams constitute only 50% of our dataset, their compound annual growth rate (99.98%) is nearly twice that of click-based scams (57.29%). Critically, reply-based scams also show the lowest detector performance--despite identifiable similarities in text content and phone number origin within categories--indicating that current off-the-shelf tools are ineffective. These results suggest that further development of detectors is necessary to defend against this rapidly changing ecosystem. By examining a range of message attributes, this work provides new insights into mobile messaging scams, informing the design of more targeted and robust detection methods. Subjects: Cryptography and Security (cs.CR); Computers and Society (cs.CY) Cite as: arXiv:2605.16656 [cs.CR]   (or arXiv:2605.16656v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.16656 Focus to learn more Submission history From: Allison Lu [view email] [v1] Fri, 15 May 2026 21:50:21 UTC (1,782 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    May 19, 2026
    Archived
    May 19, 2026
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