Die Free or Live Hard? Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers
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Bot detection is an inherently adversarial problem, and once a defense mechanism is known, adversaries can modify their modus operandi and avoid detection.
“bot detection is an inherently adversarial problem, and once a defense mechanism is known, adversaries can modify their modus operandi and avoid detection [41].”
From LOBO: Evaluation of Generalization Deficiencies in Twitter Bot Classifiers (2018) · cites this paper as [41]
Cites
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652 · see citing works
Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022 · see citing works
Leskovec, J., & Faloutsos, C. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150479 · Cited by 1,190 · see citing works
Detecting spammers on social networks
Stringhini, G., Kruegel, C., & Vigna, G. (2010) · Proceedings of the 26th Annual Computer Security Applications Conference · doi:10.1145/1920261.1920263 · Cited by 791 · see citing works
Magno, G., Rodrigues, T., & Almeida, V. A. F. (2010) · Cited by 649 · see citing works
Uncovering social spammers: social honeypots + machine learning
Lee, K., Caverlee, J., & Webb, S. (2010) · Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1835449.1835522 · Cited by 619 · see citing works
Grier, C., Thomas, K., Paxson, V., & Zhang, M. (2010) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1866307.1866311 · Cited by 558 · see citing works
Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2010) · Annual Computer Security Applications Conference · doi:10.1145/1920261.1920265 · Cited by 498 · see citing works
DON’T FOLLOW ME - Spam Detection in Twitter
Wang, A. H. (2010) · International Conference on Security and Cryptography · doi:10.5220/0002996201420151 · Cited by 469 · see citing works
Detecting and characterizing social spam campaigns
Gao, H., Hu, J. S., Wilson, C., Li, Z., Chen, Y., & Zhao, B. Y. (2010) · ACM Internet Measurement Conference (IMC) · doi:10.1145/1879141.1879147 · Cited by 420 · see citing works
The base-rate fallacy and its implications for the difficulty of intrusion detection
Axelsson, S. (1999) · Proceedings of the ACM Conference on Computer and Communications Security · doi:10.1145/319709.319710 · Cited by 327 · see citing works
Detecting spammers and content promoters in online video social networks
Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., & Gonçalves, M. (2009) · Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1571941.1572047 · Cited by 220 · see citing works
Combating spam in tagging systems
Koutrika, G., Effendi, F. A., Gyöngyi, Z., et al. (2007) · doi:10.1145/1244408.1244420 · Cited by 116 · see citing works
Identifying video spammers in online social networks
Benevenuto, F., Rodrigues, T., Almeida, V. A. F., et al. (2008) · doi:10.1145/1451983.1451996 · Cited by 100 · see citing works
Detecting Spammers and Content Promoters in Online Video Social Networks
Benevenuto, F., Rodrigues, T., Almeida, J. M., et al. (2009) · IEEE INFOCOM Workshops · doi:10.1109/infcomw.2009.5072127 · Cited by 63 · see citing works
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Empirical evaluation and new design for fighting evolving Twitter spammers
Yang, C., Harkreader, R., & Gu, G. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2267732 · Cited by 346 · see citing works
Self-citationYang, C., Harkreader, R., Zhang, J., Shin, S., & Gu, G. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187847 · Cited by 299 · see citing works
Self-citationA generic statistical approach for spam detection in Online Social Networks
Ahmed, F., & Abulaish, M. (2013) · Computer Communications · doi:10.1016/j.comcom.2013.04.004 · Cited by 199 · see citing works
Stringhini, G., Wang, G., Egele, M., Kruegel, C., Vigna, G., Zheng, H., & Zhao, B. Y. (2013) · Proceedings of the 2013 conference on Internet measurement conference · doi:10.1145/2504730.2504731 · Cited by 169 · see citing works
Contextual Outlier Interpretation
Liu, N., Shin, D., & Hu, X. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/341 · Cited by 74 · see citing works
LOBO: Evaluation of Generalization Deficiencies in Twitter Bot Classifiers
Echeverra, J., De Cristofaro, E., Kourtellis, N., Leontiadis, I., Stringhini, G., & Zhou, S. (2018) · Proceedings of the 34th Annual Computer Security Applications Conference · doi:10.1145/3274694.3274738 · Cited by 61 · see citing works
CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks
Song, J., Lee, S., & Kim, J. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813661 · Cited by 50 · see citing works
Quit Playing Games with My Heart: Understanding Online Dating Scams
Huang, J., Stringhini, G., & Yong, P. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-20550-2_12 · Cited by 48 · see citing works
- Authors
- Yang, C., Harkreader, R. C., & Gu, G.
- Affiliations
- Texas A&M University (US)
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2011
- Pages
- 318-337
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1233141674
- Cited by
- 235
- References
- 15
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 2 · 2023: 5 · 2022: 17 · 2021: 9 · 2020: 19 · 2019: 19 · 2018: 30 · 2017: 25 · 2016: 21 · 2015: 25 · 2014: 26 · 2013: 15 · 2012: 13
- FWCI
- 13.69
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- spam detection, feature design, false positive rate, spam social media
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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