Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis
Abstract
Social networks are dynamically changing over time e.g., some accounts are being created and some are being deleted or become private. This ephemerality at both an account level and content level results from a combination of privacy concerns, spam, and deceptive behaviors. In this study we analyze a large dataset of 180,340 accounts active during the Russian-Ukrainian crisis to discover a series of predictive features for the removal or shutdown of a suspicious account. We find that unlike previously reported profile and net- work features, lexical features form the basis for highly accurate prediction of the deletion of an account.
- Authors:
- Publication Date:
- Research Org.:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1431411
- Report Number(s):
- PNNL-SA-116813
453040300
- DOE Contract Number:
- AC05-76RL01830
- Resource Type:
- Conference
- Resource Relation:
- Conference: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2016), June 12-17, 2016, San Diego, California, 1-6
- Country of Publication:
- United States
- Language:
- English
- Subject:
- social media; deception; account deletion prediction; machine learning; natural language processing
Citation Formats
Volkova, Svitlana, and Bell, Eric B. Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis. United States: N. p., 2016.
Web. doi:10.18653/v1/W16-0801.
Volkova, Svitlana, & Bell, Eric B. Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis. United States. https://doi.org/10.18653/v1/W16-0801
Volkova, Svitlana, and Bell, Eric B. 2016.
"Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis". United States. https://doi.org/10.18653/v1/W16-0801.
@article{osti_1431411,
title = {Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis},
author = {Volkova, Svitlana and Bell, Eric B.},
abstractNote = {Social networks are dynamically changing over time e.g., some accounts are being created and some are being deleted or become private. This ephemerality at both an account level and content level results from a combination of privacy concerns, spam, and deceptive behaviors. In this study we analyze a large dataset of 180,340 accounts active during the Russian-Ukrainian crisis to discover a series of predictive features for the removal or shutdown of a suspicious account. We find that unlike previously reported profile and net- work features, lexical features form the basis for highly accurate prediction of the deletion of an account.},
doi = {10.18653/v1/W16-0801},
url = {https://www.osti.gov/biblio/1431411},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jun 17 00:00:00 EDT 2016},
month = {Fri Jun 17 00:00:00 EDT 2016}
}
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