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Cited by 8Recommender Systems and TechniquesSpam and Phishing DetectionCybercrime and Law Enforcement Studies

The DOI matches its Crossref record.

The Effect of Suspicious Profiles on People Recommenders

Pizzato, Luiz Augusto, Akehurst, Joshua, Silvestrini, Cameron, Yacef, Kalina, Koprinska, Irena, Kay, Judy·2012·Lecture Notes in Computer Science
DOI 10.1007/978-3-642-31454-4_19
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Integrity signals

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Crossref · 10.1007/978-3-642-31454-4_19

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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?

  • The bios of scam profiles typically include fictional descriptions of the character, often referencing religion, hobbies, desired partner traits, or the fictional reason for seeking a relationship.

    “The bios of these profiles typically include fictional descriptions of the character, often referencing religion (e.g., Koon & Yoong, 2017), hobbies (Kopp et al., 2015), desired partner traits (Pizzato et al., 2012), or the (fictional) reason for seeking a relationship (Anesa, 2020).”

    From Love as Bait: A Scoping Review and Crime Script Analysis of Online Romance Scams (2026) · cites this paper as Pizzato et al., 2012

  • Scam profiles are frequently recycled, where previously used profiles are reused or appear multiple times on the same platform with only minor edits to profile details.

    “Scam profiles are frequently recycled (1.5c), where previously used profiles are reused or appear multiple times on the same platform with only minor edits to profile details (Huang et al., 2015; Pizzato et al., 2012).”

    From Love as Bait: A Scoping Review and Crime Script Analysis of Online Romance Scams (2026) · cites this paper as Pizzato et al., 2012

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 17

  1. Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T.: Ccr - a content-collaborative reciprocal recommender for online dating. In: Proceedings of the 22nd IJCAI, Barcelona, Spain (July 2011)
  2. Brožovský, L., Petříček, V.: Recommender system for online dating service. CoRR abs/cs/0703042 (2007) · arXiv
  3. Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y.S., Compton, P., Mahidadia, A.: Collaborative Filtering for People to People Recommendation in Social Networks. In: Li, J. (ed.) AI 2010. LNCS, vol. 6464, pp. 476–485. Springer, Heidelberg (2010) · DOI
  4. Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: CHI 2009, pp. 201–210. ACM, New York (2009) · DOI
  5. Diaz, F., Metzler, D., Amer-Yahia, S.: Relevance and ranking in online dating systems. In: Proceeding of the 33rd SIGIR, pp. 66–73. ACM, New York (2010)
  6. Hernández, M.A., Stolfo, S.J.: The merge/purge problem for large databases. SIGMOD Rec. 24, 127–138 (1995) · DOI
  7. Kim, Y.S., Mahidadia, A., Compton, P., Cai, X., Bain, M., Krzywicki, A., Wobcke, W.: People Recommendation Based on Aggregated Bidirectional Intentions in Social Network Site. In: Kang, B.-H., Richards, D. (eds.) PKAW 2010. LNCS, vol. 6232, pp. 247–260. Springer, Heidelberg (2010) · DOI
  8. Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th WWW, pp. 393–402. ACM, New York (2004)
  9. McFee, B., Lanckriet, G.: Metric learning to rank. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010) (June 2010)
  10. Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7 (October 2007) · DOI
  11. O’Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technol. 4, 344–377 (2004) · DOI
  12. Pan, J., Winchester, D., Land, L., Watters, P.: Descriptive data mining on fraudulent online dating profiles. In: Proceedings of the 18th ECIS (2010)
  13. Pandit, S., Chau, D.H., Wang, S., Faloutsos, C.: Netprobe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th WWW, pp. 201–210. ACM, New York (2007)
  14. Pantel, P., Lin, D.: SpamCop: A Spam Classification and Organization Program. In: Workshop on Learning for Text Categorization (1998)
  15. Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J.: Recon: a reciprocal recommender for online dating. In: RecSys 2010: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 207–214. ACM, New York (2010) · DOI
  16. Sarawagi, S., Bhamidipaty, A.: Interactive deduplication using active learning. In: Proceedings of the 8th ACM SIGKDD, New York, pp. 269–278 (2002) · DOI
  17. Toma, C.L., Hancock, J.T., Ellison, N.B.: Separating fact from fiction: An examination of deceptive self-presentation in online dating profiles. Personality and Social Psychology Bulletin 34(8), 1023–1036 (2008) · DOI · Paper page

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Metadata

Authors
Pizzato, L. A., Akehurst, J., Silvestrini, C., Yacef, K., Koprinska, I., & Kay, J.
Affiliations
The University of Sydney (AU)
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Berlin Heidelberg
Published
2012
Pages
225-236
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-642-31454-4_19
OpenAlex
W2062085026
Cited by
8
References
0
Citations by year
2025: 1 · 2024: 1 · 2020: 1 · 2015: 1 · 2013: 3 · 2012: 1
FWCI
1.26
Type
conference paper
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Social Sciences › Law › Cybercrime and Law Enforcement Studies
Keywords
romance scams, online dating, recommender systems, fake accounts, collaborative filtering, content-based recommendation, fraud detection

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