TY - GEN
T1 - Fifteen minutes of unwanted fame
T2 - 2017 ACM Internet Measurement Conference, IMC 2017
AU - Snyder, Peter
AU - Kanich, Chris
AU - Doerfler, Periwinkle
AU - McCoy, Damon
N1 - Funding Information:
This work was supported in part by National Science Foundation grants CNS-1717062, CNS-1237265 and CNS-1619620, AWS Cloud Credits for Research, and gifts from Google
Publisher Copyright:
© 2017 Copyright held by the owner/author(s).
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Doxing is online abuse where a malicious party harms another by releasing identifying or sensitive information. Motivations for doxing include personal, competitive, and political reasons, and web users of all ages, genders and internet experience have been targeted. Existing research on doxing is primarily qualitative. This work improves our understanding of doxing by being the first to take a quantitative approach. We do so by designing and deploying a tool which can detect dox files and measure the frequency, content, targets, and effects of doxing on popular dox-posting sites. This work analyzes over 1.7 million text files posted to pastebin.com, 4chan.org and 8ch.net, sites frequently used to share doxes online, over a combined period of approximately thirteen weeks. Notable findings in this work include that approximately 0.3% of shared files are doxes, that online social networking accounts mentioned in these dox files are more likely to close than typical accounts, that justice and revenge are the most often cited motivations for doxing, and that dox files target males more frequently than females. We also find that recent anti-abuse efforts by social networks have reduced how frequently these doxing victims closed or restricted their accounts after being attacked. We also propose mitigation steps, such a service that can inform people when their accounts have been shared in a dox file, or law enforcement notification tools to inform authorities when individuals are at heightened risk of abuse.
AB - Doxing is online abuse where a malicious party harms another by releasing identifying or sensitive information. Motivations for doxing include personal, competitive, and political reasons, and web users of all ages, genders and internet experience have been targeted. Existing research on doxing is primarily qualitative. This work improves our understanding of doxing by being the first to take a quantitative approach. We do so by designing and deploying a tool which can detect dox files and measure the frequency, content, targets, and effects of doxing on popular dox-posting sites. This work analyzes over 1.7 million text files posted to pastebin.com, 4chan.org and 8ch.net, sites frequently used to share doxes online, over a combined period of approximately thirteen weeks. Notable findings in this work include that approximately 0.3% of shared files are doxes, that online social networking accounts mentioned in these dox files are more likely to close than typical accounts, that justice and revenge are the most often cited motivations for doxing, and that dox files target males more frequently than females. We also find that recent anti-abuse efforts by social networks have reduced how frequently these doxing victims closed or restricted their accounts after being attacked. We also propose mitigation steps, such a service that can inform people when their accounts have been shared in a dox file, or law enforcement notification tools to inform authorities when individuals are at heightened risk of abuse.
KW - Doxing
KW - Identity theft
KW - Online abuse
UR - http://www.scopus.com/inward/record.url?scp=85038616131&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85038616131&partnerID=8YFLogxK
U2 - 10.1145/3131365.3131385
DO - 10.1145/3131365.3131385
M3 - Conference contribution
AN - SCOPUS:85038616131
T3 - Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
SP - 432
EP - 444
BT - IMC 2017 - Proceedings of the 2017 Internet Measurement Conference
PB - Association for Computing Machinery
Y2 - 1 November 2017 through 3 November 2017
ER -