Detecting Fake Profiles On Online Matrimony

1. Detecting Fake Profiles On Online Matrimony Vaibhav Garg Dr. Ponnurangam Kumaraguru (Chair) linkedin.com/in/vaibhav-garg- 0a708899 facebook.com/in/vaibhav.gar g.104203 @rk_check

2. 2 Thesis Committee ◆ Dr. Arun Balaji Buduru, IIIT Delhi ◆ Dr. Siddhartha Asthana, United Health Group (Optum) ◆ Dr. Ponnurangam Kumaraguru, IIIT Delhi

4. 4 Core Thesis Question How to automatically detect fake profiles on online matrimony ?

5. 5 Demo * Due to the privacy policy of the company, we can not give demo on the actual company’s portal.

6. Outline ◆ About Online Matrimony ◆ About the Data ◆ Characteristics of a fake profile ◆ Using only Behaviour Trends ◆ Using Behavior, Edit and Profile Information ◆ Incorporating Community features ◆ Feature Engineering: Proposed Full length feature vector ◆ Final Results ◆ Conclusion 6

7. 7 Register Suggested View Profile Start Conversation

9. 9 About the Data ◆ To dig into the problem, we chose a use case of India’s leading matrimony website ◆ Ground Truth: 5,40,737 genuine profiles and very less number of fake profiles. ◆ Data of Categorical Attributes : age, body type, caste, city, country, education, height, income, manglik, marital status, mother tongue, occupation, religion.

10. Categorical Data 10 Attribute Number of Categories Different Categories Caste 470 Hindu: Arora, Hindu: Aggarwal, Hindu: Brahmin etc. Height 37 5’0, 5’1, 5’2, 5’3 etc. Income 25 Rs. 0 - 1 Lakh, Rs 1-2 Lakh etc Mother Tongue 42 Telugu, Bengali, Hindi-Delhi etc. Occupation 69 Doctor, Analyst, IT-Engineer etc.

11. Categorical Data 11 Attribute Number of Categories Different Categories Religion 10 Hindu, Muslim, Christian etc. Body Type 4 Slim, Average, Athletic, Heavy Country 214 India, Afghanistan, Australia etc. City 3683 Delhi, UP, Ahmedabad etc. Manglik 2 Manglik, Non-Manglik

12. Categorical Data 12 Attribute Number of Categories Different Categories Marital Status 4 Never Married, Divorcee, Separated and Widowed Education 53 B.A, B.Com, B.Tech etc.

14. 14 Behaviour Heterogeneity C1 Genuine Profile Fake Profile C2 C3 C4 C8 C7 C6 C5 C1 C2 C3

15. 15 Inconsistent Edits Edit Done After 4 Days of Registration

16. 16 Profile Inconsistency

18. 18 Behavioural Trend for Caste Attribute Experimented on 100 fake and 100 genuine profiles belonging to Aggarwal Community

19. 19 Behavioural Trend for Marital Status Attribute Experimented on 100 fake and 100 genuine profiles belonging to Non Married Community

20. 20 Static Windows User’s First 8 days Activity First 12 hours Day 0 … . . . . . 0th window 1st window Day 0 Activity Day 1 Activity Day 6 Activity Day 7 Activity … . . . . . Last 12 hours Day 0 First 12 hours Day 7 Last 12 hours Day 7 15th window 16th window

21. 21 Static Windows and Feature Generation

22. 22 Which Model to Choose ?

23. Model Architecture 23 Output Features

24. Offline Results on Behaviour Features 24 Confusion Matrix Predicted Fake Predicted Clean Actual Fake 2953 852 Actual Clean 168 17799 Above results are obtained on 3805 fake profiles and 17967 clean profiles Drawback: The user has to be 8 days old on portal to be scrutinized through this approach

25. LIVE Results : True Positives 25

26. LIVE Results : False Negatives 26 Edit and Profile features needs to be incorporated !!

28. 28 Edit Summary for Mother Tongue Attribute Experimented on 100 fake and 100 genuine profiles which registered with Hindi-UP category

29. 29 Edit Summary for Income Attribute Experimented on 100 fake and 100 genuine profiles which registered with Rs 5-7.5 Lakh category

30. 30 Concept of Dynamic Windows User’s Active Lifetime on portal = T seconds User’s total initiates = N Time period of first N/W initiates If we select no of windows = W Time period of next N/W initiates Time period of last N/W initiates … . . . . . 0th window 1st window last window

31. Feature Designing ◆ Profile Features : One hot vector of profile attributes ◆ Behavior Features : In dynamic time windows, each feature stores the proportion of initiates sent to a particular category of attribute ◆ Edit Features : In dynamic time windows, each feature stores the proportion of time user has spent on that particular category of attribute ◆ Other Raw Features : In each window, we also store the total interests sent and time duration of that window. 31

32. 32 Feature Designing 0th window + + . . . . Nth window

33. 33 Experimenting with number of dynamic windows No of Windows Precision Recall Accuracy Using 5 windows 0.170 0.510 0.8830 Using 4 window 0.192 0.635 0.8891 Using 3 windows 0.230 0.780 0.8977 Using 2 windows 0.242 0.804 0.8975 Using 1 window 0.266 0.866 0.8972

34. 34 Feature Selection on Best Model Method Precision Recall Accuracy Best Model 0.266 0.866 0.8972 Best Model + Feature Selection 0.269 0.894 0.9083 Criteria Used = (Entropy for fake) - (Entropy for clean) (Entropy for fake) Precision is still low !!

36. 36 Affinity Features along with Behaviour Features ◆ An Affinity score between two categories i and j is the likelihood score of a person having category i to send interests to user having category j ◆ Affinity scores when incorporated with behaviour features compare between how a user is expected to behave and how he/she actually behaves on the platform

37. 37 Affinity Features

39. 39 Proposed Full length Feature Vector Profile Features Behaviour Features in Time windows Affinity Features Edit Features in Time windows + + +

40. 40 Final Model Architecture

42. 42 Final Results Method Precision Recall Accuracy Proposed Features + Autoencoder 0.341 0.902 0.9176 Product team demanded for 25% precision at 60% recall !!

44. Conclusion ◆ We first studied the distinction in behaviour, profile and edit pattern between genuine and fake users ◆ We incorporated these characteristics in the form of features using dynamic time windows. ◆ We then trained the autoencoder model to detect fake profiles on online matrimony. 44

45. 45 Real World Impact Week 1 Week 2

46. 46 Real World Impact Week 3 Week 4

47. Limitations and Future Work ◆ More number of samples for training autoencoder can lead to more generalisation. ◆ We detected fake profiles using categorical attributes only. Text spamming can be explored. 47

48. Acknowledgement ◆ Committee Members ◆ Hunny, Adhish from InfoEdge India Ltd. ◆ Members of Precog family ◆ Family and friends 48

49. 49 References ◆ https://timesofindia.indiatimes.com/city/hyderabad/nigerian-held-for-matrim onial-fraud-in-hyderabad/articleshow/66939563.cms ◆ https://www.hindustantimes.com/mumbai-news/woman-creates-fake-profil e-on-matrimony-site-cheats-mumbai-man-of-rs23-lakh/story-KHLj4zPWI8U Gv31YM5A8tK.html ◆ https://timesofindia.indiatimes.com/city/mangaluru/online-matrimony-frauds -on-the-rise-in-mangaluru/articleshow/66102334.cms ◆ https://timesofindia.indiatimes.com/city/pune/matrimonial-fraud-on-the-rise- more-than-50-cases-registered-this-year/articleshow/60049950.cms ◆ https://dl.acm.org/citation.cfm?id=2689747 ◆ https://link.springer.com/book/10.1007%2F978-3-319-20466-6 ◆ https://dl.acm.org/citation.cfm?id=3106489

50. Thanks! vaibhav17064@iiitd.ac.in 50

Detecting Fake Profiles On Online Matrimony

Detecting Fake Profiles On Online Matrimony

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