User Anonymity on Twitter

Sai Teja Peddinti; Keith W. Ross; Justin Cappos

doi:10.1109/MSP.2017.74

User Anonymity on Twitter

Sai Teja Peddinti, Keith W. Ross, Justin Cappos

Research output: Contribution to journal › Article › peer-review

Abstract

A novel machine-based classifier system leverages Twitter user anonymity patterns and their correlation to content sensitivity to automatically identify accounts that tweet sensitive content. Anonymity's role in society and the nuances and complexity of content sensitivity are confirmed.

Original language	English (US)
Article number	7945182
Pages (from-to)	84-87
Number of pages	4
Journal	IEEE Security and Privacy
Volume	15
Issue number	3
DOIs	https://doi.org/10.1109/MSP.2017.74
State	Published - 2017

Keywords

Twitter
anonymity
anonymous
content sensitivity
privacy
pseudonymous
real-name policy
social media
social networks

ASJC Scopus subject areas

Computer Networks and Communications
Electrical and Electronic Engineering
Law

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10.1109/MSP.2017.74

Cite this

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title = "User Anonymity on Twitter",

abstract = "A novel machine-based classifier system leverages Twitter user anonymity patterns and their correlation to content sensitivity to automatically identify accounts that tweet sensitive content. Anonymity's role in society and the nuances and complexity of content sensitivity are confirmed.",

keywords = "Twitter, anonymity, anonymous, content sensitivity, privacy, pseudonymous, real-name policy, social media, social networks",

author = "Peddinti, {Sai Teja} and Ross, {Keith W.} and Justin Cappos",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2017",

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KW - real-name policy

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User Anonymity on Twitter

Abstract

Keywords

ASJC Scopus subject areas

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