• Corpus ID: 15568493

Fraud Detection in Online Reviews using Machine Learning Techniques

@inproceedings{Shivagangadhar2015FraudDI,
  title={Fraud Detection in Online Reviews using Machine Learning Techniques},
  author={Kolli Shivagangadhar and Sohan Sathyan},
  year={2015}
}

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References

SHOWING 1-10 OF 10 REFERENCES
Review spam detection via temporal pattern discovery
TLDR
It is discovered that singleton review is a significant source of spam reviews and largely affects the ratings of online stores and a hierarchical algorithm is proposed to robustly detect the time windows where such attacks are likely to have happened.
What Yelp Fake Review Filter Might Be Doing?
TLDR
This work attempts to find out what Yelp might be doing by analyzing its filtered reviews and postulates that Yelp’s filtering is reasonable and its filtering algorithm seems to be correlated with abnormal spamming behaviors.
Spotting fake reviewer groups in consumer reviews
TLDR
This paper studies spam detection in the collaborative setting, i.e., to discover fake reviewer groups by using several behavioral models derived from the collusion phenomenon among fake reviewers and relation models based on the relationships among groups, individual reviewers, and products they reviewed to detectfake reviewer groups.
Detecting product review spammers using rating behaviors
TLDR
This paper identifies several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers, and shows that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.
Distributional Footprints of Deceptive Product Reviews
TLDR
A range of experiments confirm the hypothesized connection between the distributional anomaly and deceptive reviews and provide novel quantitative insights into the characteristics of natural distributions of opinions in the TripAdvisor hotel review and the Amazon product review domains.
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
TLDR
This work develops and compares three approaches to detecting deceptive opinion spam, and develops a classifier that is nearly 90% accurate on the authors' gold-standard opinion spam dataset, and reveals a relationship between deceptive opinions and imaginative writing.
Support Vector Machine) http://en.wikipedia.org/wiki/Support_vector_machine [4] Yelp Challenge Dataset http://www.yelp.com/dataset_challenge [5] "Opinion Spam and Analysis
  • Support Vector Machine) http://en.wikipedia.org/wiki/Support_vector_machine [4] Yelp Challenge Dataset http://www.yelp.com/dataset_challenge [5] "Opinion Spam and Analysis
  • 2008
Wikipedia-n-gram http://en.wikipedia.org/wiki
  • Wikipedia-n-gram http://en.wikipedia.org/wiki
Wikipedia-Supervised Learning
  • Wikipedia-Supervised Learning