@inproceedings{dc555b8372454f229fd4b4887f11b904,
title = "Finding sensitive accounts on Twitter: An automated approach based on follower anonymity",
abstract = "We explore the feasibility of automatically finding accounts that publish sensitive content on Twitter, by examining the percentage of anonymous and identifiable followers the accounts have. We first designed a machine learning classifier to automatically determine if a Twitter account is anonymous or identifiable. We then classified an account as potentially sensitive based on the percentages of anonymous and identifiable followers the account has. We applied our approach to approximately 100,000 accounts with 404 million active followers. The approach uncovered accounts that were sensitive for a diverse number of reasons.",
author = "Peddinti, {Sai Teja} and Ross, {Keith W.} and Justin Cappos",
note = "Publisher Copyright: {\textcopyright} Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 10th International Conference on Web and Social Media, ICWSM 2016 ; Conference date: 17-05-2016 Through 20-05-2016",
year = "2016",
language = "English (US)",
series = "Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016",
publisher = "AAAI press",
pages = "655--658",
booktitle = "Proceedings of the 10th International Conference on Web and Social Media, ICWSM 2016",
}