@inproceedings{c5031b803f984384b2443f841832797b,
title = "Indirect information linkage for OSINT through authorship analysis of aliases",
abstract = "In this paper we examine the problem of automatically linking online accounts for open source intelligence gathering. We specifically aim to determine if two social media accounts are shared by the same author, without the use of direct linking evidence. We profile the accounts using authorship analysis and find the best matching guess. We apply this to a series of Twitter accounts identified as malicious by a methodology named SPOT and find several pairs of accounts that belong to the same author, despite no direct evidence linking the two. Overall, our results show that linking aliases is possible with an accuracy of 84%, and using our automated threshold method improves our accuracy to over 90% by removing incorrectly discovered matches.",
author = "Robert Layton and Charles Perez and Babiga Birregah and Paul Watters and Marc Lemercier",
year = "2013",
doi = "10.1007/978-3-642-40319-4_4",
language = "English",
isbn = "9783642403187",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "36--46",
editor = "Jiuyong Li and Longbing Cao and Can Wang and Tan, {Kay Chen} and Bo Liu and Jian Pei and Tseng, {Vincent S.}",
booktitle = "Trends and applications in knowledge discovery and data mining",
address = "United States",
note = "17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 ; Conference date: 14-04-2013 Through 17-04-2013",
}