CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1145/2487575.2487580

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Flagged first, then by citations

3 of the 47 references couldn’t be matched to a known paper.

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

Cited by

Sorted by citations

See all 426

Related

See all

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

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