The DOI matches its Crossref record; 68 of the 69 references it cites were found in a registry or an index.
Automated Crowdturfing Attacks and Defenses in Online Review Systems
Open accessAbstract
Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For the inter-review features, the statistics computed over L i j leverage the observation that fraudsters synchronize the activities of the accounts that they control, e.g., in a”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [71]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · cited 1×
“Relevancy is estimated by calculating lexical similarity using WordNet [44], a widely used lexical database that groups English words into sets of synonyms and measures their concept relatedness [51].”
Method(2016) · Cited by 11,679
The MovieLens Datasets: History and Context
Harper, F., & Konstan, J. (2016) · TIIS · doi:10.1145/2827872 · Cited by 5,938
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., & Bengio, Y. (2012) · arXiv · doi:10.48550/arxiv.1211.5063 · Cited by 4,310
Learning Word Vectors for Sentiment Analysis
Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011) · Cited by 3,710
Generating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining
Baccianella, S., Esuli, A., & Sebastiani, F. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2jktnrykui9s · Cited by 2,689
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Wang, W. Y. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2067 · Cited by 2,010
The Development and Psychometric Properties of LIWC2015
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015) · University of Texas at Austin · doi:10.15781/t25p41 · Cited by 1,674
Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542
An Empirical Exploration of Recurrent Network Architectures
Józefowicz, R., Zaremba, W., & Sutskever, I. (2015) · Cited by 1,459
Syntactic clustering of the Web
Broder, A., Glassman, S. C., Manasse, M. S., & Zweig, G. (1997) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(97)00031-7 · Cited by 1,348
Generating Text with Recurrent Neural Networks
Sutskever, I., Martens, J., & Hinton, G. E. (2011) · Cited by 1,266
Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud
Luca, M., & Zervas, G. (2016) · Management Science · doi:10.1287/mnsc.2015.2304 · Cited by 1,263
Exploring the Limits of Language ModelingPreprint
Józefowicz, R., Vinyals, O., Schuster, M., Shazeer, N., & Wu, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.02410 · Cited by 1,238
Building Natural-Language Generation SystemsPreprint
Reiter, E. (1996) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9605002 · Cited by 1,233
Neural Responding Machine for Short-Text Conversation
Shang, L., Lu, Z., & Li, H. (2015) · doi:10.3115/v1/p15-1152 · Cited by 1,146
Schleimer, S., S, W. D., & Aiken, A. (2003) · Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data · doi:10.1145/872757.872770 · Cited by 1,099 · cited 1×
“We use Winnowing [63], a widely used method to identify duplicate or near-duplicate text.”
MethodSemantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Wen, T.-H., Gašić, M., Mrkšić, N., Su, P.-H., Vandyke, D., & Young, S. J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1199 · Cited by 974
Ted Pedersen, A., Patwardhan, S., & Michelizzi, J. (2004) · doi:10.3115/1614025.1614037 · Cited by 841
Building Natural Language Generation Systems
Reiter, E., & Dale, R. (2000) · Cambridge University Press eBooks · doi:10.1017/cbo9780511519857 · Cited by 822
Inferring Networks of Substitutable and Complementary Products
McAuley, J., Pandey, R., & Leskovec, J. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783381 · Cited by 645
Collective Opinion Spam Detection: Bridging Review Networks and Metadata
Rayana, S., & Akoglu, L. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783370 · Cited by 604
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., & Shet, V. (2013) · arXiv · doi:10.48550/arxiv.1312.6082 · Cited by 582
What Yelp Fake Review Filter Might Be Doing?
Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14389 · Cited by 570
Neural Text Generation from Structured Data with Application to the Biography Domain
Lebret, R., Grangier, D., & Auli, M. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1128 · Cited by 534
Jindal, N., & Liu, B. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242759 · Cited by 481
Spotting opinion spammers using behavioral footprints
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487580 · Cited by 426
Fact Checking: Task definition and dataset construction
Vlachos, A., & Riedel, S. (2014) · Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science · doi:10.3115/v1/w14-2508 · Cited by 402
Learning to identify review spam
Li, F., Huang, M., Yang, Y., & Zhu, X. (2011) · doi:10.5591/978-1-57735-516-8/ijcai11-414 · Cited by 391
Review Graph Based Online Store Review Spammer Detection
Wang, G., Xie, S., Liu, B., & Yu, P. S. (2011) · Proceedings · doi:10.1109/icdm.2011.124 · Cited by 390
Towards a General Rule for Identifying Deceptive Opinion Spam
Li, J., Ott, M., Cardie, C., & Hovy, E. H. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1147 · Cited by 383
Yuan, Z., Lü, Y., Wang, Z., & Xue, Y. (2014) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2619239.2631434 · Cited by 373
Exploiting Burstiness in Reviews for Review Spammer Detection
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., & Ghosh, R. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14400 · Cited by 356
Mihalcea, R. F., & Strapparava, C. (2009) · Joint Conference of the Annual Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (ACL-IJCNLP) · doi:10.3115/1667583.1667679 · Cited by 340
Comparison deep learning method to traditional methods using for network intrusion detection
Dong, B., & Wang, X. (2016) · IEEE International Conference on Communication Software and Networks · doi:10.1109/iccsn.2016.7586590 · Cited by 289
Empirical evaluation and combination of advanced language modeling techniques
Mikolov, T., Deoras, A., Kombrink, S., Burget, L., & Černocký, J. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-242 · Cited by 278
Choosing words in computer-generated weather forecasts
Reiter, E., Sripada, S. G., Jim Hunter, A., Yu, J. S., & Davy, I. P. (2005) · Artificial Intelligence · doi:10.1016/j.artint.2005.06.006 · Cited by 262
Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks
Melicher, W., Ur, B., Segreti, S. M., Komanduri, S., Bauer, L., Christin, N., & Cranor, L. F. (2016) · USENIX Security Symposium · Cited by 252
Recognizing functions in binaries with neural networks
Shin, E. C. R., Song, D. X., & Moazzezi, R. (2015) · USENIX Security Symposium · Cited by 223
Anderson, H. S., Woodbridge, J., & Filar, B. (2016) · ACM Workshop on Artificial Intelligence and Security (AISec) · doi:10.1145/2996758.2996767 · Cited by 209
Kannan, A., Kurach, K., Ravi, S., Kaufmann, T., Tomkins, A., Miklos, B., Corrado, G. S., Lukacs, L., Ganea, M., Young, P., et al. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939801 · Cited by 200
Serf and turf: crowdturfing for fun and profit
Wang, G., Wilson, C., Zhao, X., Zhu, Y., Mohanlal, M., Zheng, H., & Zhao, B. Y. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187928 · Cited by 197
Self-citationDeep Neural Network Language Models
Arısoy, E., Sainath, T. N., Kingsbury, B. E. D., & Ramabhadran, B. (2012) · Cited by 174
Hierarchical Neural Network Generative Models for Movie Dialogues.Preprint
Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2015) · arXiv (Cornell University) · Cited by 143
Man vs. machine: practical adversarial detection of malicious crowdsourcing workers
Wang, G., Wang, T., Zhang, H., & Zhao, B. Y. (2014) · Cited by 142
Self-citationKim, G., Yi, H., Lee, J., Paek, Y., & Yoon, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01726 · Cited by 130
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue SystemsPreprint
Wen, T.-H., Gašić, M., Mrkšić, N., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.01745 · Cited by 124
Dirty jobs: the role of freelance labor in web service abuse
Motoyama, M., McCoy, D., Levchenko, K., Savage, S., & Voelker, G. M. (2011) · Cited by 94
Generating Approximate Geographic Descriptions
Turner, R., Sripada, S. G., & Reiter, E. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15573-4_7 · Cited by 91
Lee, K., Tamilarasan, P., & Caverlee, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14384 · Cited by 81
Key Challenges in Defending Against Malicious Socialbots
Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2012) · doi:10.5281/zenodo.3264613 · Cited by 76
Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program FeaturesPreprint
Saxe, J., & Berlin, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.03096 · Cited by 75
Fake Reviews: The Malicious Perspective
Lappas, T. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31178-9_3 · Cited by 61
Exploring linguistic features for web spam detection
Piskorski, J., Sydow, M., & Weiss, D. (2008) · doi:10.1145/1451983.1451990 · Cited by 61
Goga, O., Venkatadri, G., & Gummadi, K. P. (2015) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2815675.2815699 · Cited by 60
Hovy, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2057 · Cited by 47
Strength in Numbers: Robust Tamper Detection in Crowd Computations
Viswanath, B., Bashir, M. A., Zafar, M. B., Bouget, S., Guha, S., Gummadi, K. P., Kate, A., & Mislove, A. (2015) · Proceedings of the 2015 ACM on Conference on Online Social Networks · doi:10.1145/2817946.2817964 · Cited by 43
Self-citationKakhki, A. M., Kliman-Silver, C., & Mislove, A. E. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488468 · Cited by 42
Lee, K., Webb, S., & Ge, H. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14528 · Cited by 39
To catch a fake: Curbing deceptive Yelp ratings and venues
Rahman, M., Cărbunar, B., Ballesteros, J., & Chau, D. H. (2015) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11264 · Cited by 26
DSpin: Detecting Automatically Spun Content on the Web
Zhang, Q., Wang, D. Y., & Voelker, G. M. (2014) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2014.23004 · Cited by 24
Capturing Meaning in Product Reviews with Character-Level Generative Text Models.Preprint
Lipton, Z. C., Vikram, S., & McAuley, J. (2015) · arXiv (Cornell University) · Cited by 23
A Novel LSTM-RNN Decoding Algorithm in CAPTCHA Recognition
Rui, C., Yang, J., Rong-gui, H., & Huang, S. (2013) · International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC) · doi:10.1109/imccc.2013.171 · Cited by 19
Choosing words in computer-generated weather forecasts
ReiterEhud, SripadaSomayajulu, HunterJim, 유진, & DavyIan (2005) · doi:10.5555/1113166.1644548 · Cited by 5
Research works printed but not linked: 4
- Oana Goga, Giridhari Venkatadri, and Krishna P. Gummadi. 2015. The doppelg"anger bot attack: Exploring identity impersonation in online social networks Proc. of IMC.
- Ian J. Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press.
- Arash Molavi Kakhki, Chloe Kliman-Silver, and Alan Mislove. 2013. Iolaus: Securing online content rating systems. In Proc. of WWW.
- Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey T. Hancock. 2011. Finding deceptive opinion spam by any stretch of the imagination Proc. of NAACL-HLT.
Cited by
Sorted by citations
Artificial intelligence, cyber-threats and industry 4.0: challenges and opportunities
Bécue, A., Praça, I., & Gama, J. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09942-2 · Cited by 368
False Information on Web and Social Media: A Survey
Kumar, S., & Shah, N. (2018) · arXiv · doi:10.48550/arxiv.1804.08559 · Cited by 289
The Emerging Threat of Ai-driven Cyber Attacks: A Review
Guembe, B., Azeta, A., Misra, S., Osamor, V. C., Fernandez-Sanz, L., & Pospelova, V. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2037254 · Cited by 267
Unpacking the Social Media Bot: A Typology to Guide Research and Policy
Gorwa, R., & Guilbeault, D. (2020) · Policy & Internet · doi:10.1002/poi3.184 · Cited by 270
Anticipating and Addressing the Ethical Implications of Deepfakes in the Context of Elections
Diakopoulos, N., & Johnson, D. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3474183 · Cited by 194
Metadata
- Authors
- Yao, Y., Viswanath, B., Cryan, J., Zheng, H., & Zhao, B. Y.
- Affiliations
- University of Chicago (US)
- University of Chicago, Chicago, IL, USA
- Venue
- Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2017-10-30
- Pages
- 1143-1158
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W2752337926
- Semantic Scholar
- 352b2cfd65b5a1231e5e64bb1b9d6ed71e5a60a2
- Cited by
- 150
- References
- 69
- Citations by year
- 2025: 10 · 2024: 6 · 2023: 20 · 2022: 21 · 2021: 27 · 2020: 19 · 2019: 30 · 2018: 17
- FWCI
- 13.86
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NSF (CNS-1527939, CNS-1705042)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- fake reviews, recurrent neural networks, misinformation, online review systems, attack detection, burstiness
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review