{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/styleml-stylometry-with-structure-and","title":"SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis","arxiv_id":"2104.00764","date":"2021-04-01","proceeding":"EMNLP 2021 11","authors":["Pranav Maneriker","Yuntian He","Srinivasan Parthasarathy"],"abstract":"Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils). Additionally, users migrate to new forums when one is closed, making it difficult to link users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and interaction modeling using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution. 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