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Cited by 426Spam and Phishing DetectionMisinformation and Its ImpactsHate Speech and Cyberbullying Detection

The DOI matches its Crossref record; 44 of the 47 references it cites were found in a registry or an index.

Spotting opinion spammers using behavioral footprints

Mukherjee, Arjun, Kumar, Abhinav, Liu, Bing, Wang, Junhui, Hsu, Meichun, Castellanos, Malu, Ghosh, Riddhiman·2013·Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
DOI 10.1145/2487575.2487580
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Abstract

Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or to demote some target products. In recent years, fake review detection has attracted significant attention from both the business and research communities. However, due to the difficulty of human labeling needed for supervised learning and evaluation, the problem remains to be highly challenging. This work proposes a novel angle to the problem by modeling spamicity as latent. An unsupervised model, called Author Spamicity Model (ASM), is proposed. It works in the Bayesian setting, which facilitates modeling spamicity of authors as latent and allows us to exploit various observed behavioral footprints of reviewers. The intuition is that opinion spammers have different behavioral distributions than non-spammers. This creates a distributional divergence between the latent population distributions of two clusters: spammers and non-spammers. Model inference results in learning the population distributions of the two clusters. Several extensions of ASM are also considered leveraging from different priors. Experiments on a real-life Amazon review dataset demonstrate the effectiveness of the proposed models which significantly outperform the state-of-the-art competitors.

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Research works printed but not linked: 6

  1. Duda R. O. Hart P. E. and Stork D.J. 2001. Pattern Recognition. Wiley. Duda R. O. Hart P. E. and Stork D.J. 2001. Pattern Recognition. Wiley.
  2. 10.5555/2283696.2283811 · DOI
  3. Ramage , D. , Hall , D. , Nallapati , R. , & Manning , C.D. 2009. A supervised topic model for credit attribution in multi-labeled corpora. EMNLP ( 2009 ). Ramage, D., Hall, D., Nallapati, R., & Manning, C.D. 2009. A supervised topic model for credit attribution in multi-labeled corpora. EMNLP (2009).
  4. Streitfeld , D. 2012. Buy Reviews on Yelp , Get Black Mark. ( 2012 ). Streitfeld, D. 2012. Buy Reviews on Yelp, Get Black Mark. (2012).
  5. 10.5555/1814490.1814493 · DOI
  6. 10.5555/945365.964285 · DOI

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Metadata

Authors
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R.
Affiliations
  • University of Illinois Chicago (US)
  • Hewlett-Packard (United States) (US)
  • HP Labs, Palo Alto, CA, USA
ORCID
Venue
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Publisher
ACM
Published
2013-08-11
Pages
632-640
DOI
10.1145/2487575.2487580
OpenAlex
W2016266039
Cited by
426
References
47
Citations by year
2026: 3 · 2025: 11 · 2024: 13 · 2023: 21 · 2022: 37 · 2021: 37 · 2020: 61 · 2019: 47 · 2018: 55 · 2017: 47 · 2016: 35 · 2015: 40 · 2014: 17 · 2013: 1
FWCI
24.84
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
Funders
National Science Foundation
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 review detection, Bayesian clustering, reviewer behavior, Amazon reviews

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