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Pinning Down Abuse on Google Maps Univ of California, San Diego - PowerPoint PPT Presentation

Pinning Down Abuse on Google Maps Univ of California, San Diego Danny Y. Huang, Kirill Levchenko, Alex C. Snoeren Google Doug Grundman, Kurt Thomas, Abhishek Kumar, Elie Bursztein Presented at the International World Wide Web Conference in


  1. Pinning Down Abuse on Google Maps Univ of California, San Diego Danny Y. Huang, Kirill Levchenko, Alex C. Snoeren Google Doug Grundman, Kurt Thomas, Abhishek Kumar, Elie Bursztein Presented at the International World Wide Web Conference in Perth, Australia on April 7, 2017.

  2. Maps SEO is based on physical proximity Traditional Maps SEO SEO Ranked by relevance Ranked by proximity Example: pharma Example: locksmiths How many and where? How to make money? How to evade detection? Impact on users? 2

  3. 7 locksmiths near me 3

  4. “Locksmith” located at UPS Store 4

  5. Fake locksmiths are an old problem Jan 2016 False sense of proximity Monetize from inferior service 5

  6. Violation of ToS Caused harm to users Overview of abuse on Google Maps 6

  7. Overview of abuse on Google Maps Fake Locations (40%) locksmiths, plumbers, Abusive listings garage door openers 2014 - 2015 Affiliate Fraud (13%) 15% reached hotels, restaurants users Misc (47%) 7

  8. Affiliate fraudsters hijack hotel listings Reputable travel agency Paying for referral traffic Damages to branding 8

  9. Abuse varies across countries locksmiths plumbers, etc USA India 0% 20% 40% 60% 80% 100% % of abusive listings in each country % of abusive listings in each country hotels 9

  10. How did scammers evade Google’s detection? 10

  11. Creating a free listing on Google Maps 11

  12. Verification via mail 80% abusive listings verified through mail 12

  13. Creating listings by buying fake locations Residential Commercial 13

  14. Verifying listings using the same address Impose a cutoff? 123 Main St 123 Main St Suite 456 Verify multiple listings 123 Main St #456 #456 123 Main St 25% of abusive listings l 23 Main St used this trick 14

  15. Abusive listings verified at the same address e.g. legit doctor # Listings at listings at hospital same address >1,000 101 - 1,000 11 - 100 2 - 10 1 0% 20% 40% 60% 80% 100% Probability of abusive listings 15

  16. Impact on end users? 16

  17. Quantifying impact on users # of abusive search results # of search results Locksmiths * 11% 17 * 11% of local search results for locksmiths in the US were abusive during our analysis period.

  18. Search results that are abusive New York, NY Locksmiths Plumbers, etc 11% 0.4% 16% West Harrison, NY * 84% Restaurants Hotels 1% 1% 18 * 84% of search results for locksmiths in West Harrison, NY were abusive during our analysis period.

  19. Pie chart: abusive results that are restaurants Locksmiths Plumbers, etc 11% 0.4% * * Restaurants Hotels 1% 1% 19 * For every 100 random abusive search results in the US, 2 were abusive locksmiths, while 47 were abusive restaurants.

  20. Overall search results that are abusive Locksmiths Plumbers, etc 11% 0.4% 0.5% of search results in US were abusive * Restaurants Hotels 1% 1% 20 * Out of 1000 random search results in the US, only 5 were abusive results during our analysis period.

  21. Summary SEO based on physical proximity Fake locations vs affiliate fraud Hard to verify addresses at scale Improved defenses by Google; abusive listings down by 70% from peak 21

  22. Thank you! More info: http://sysnet.ucsd.edu/~dhuang/ Univ of California, San Diego Danny Y. Huang, Kirill Levchenko, Alex C. Snoeren Google Doug Grundman, Kurt Thomas, Abhishek Kumar, Elie Bursztein

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