1. Collating Social Network Profiles

2. Objective 2 System

3. Objective 3 Company Name System Social Network Profiles InputOutput

4. 4 Record Linkage + Identity

5. Agenda 5 Introduction Objective Contrast to Existing Work Work Done Baseline System Individual Network Approach Machine Learning Experiments Next Steps, Q&A

6. Baseline System 6

7. Ground Truth  Two networks: Facebook and Twitter  Top seventy 2013 Fortune 500 companies  Two networks: Facebook and Twitter  Top seventy 2013 Fortune 500 companies 7

8. Baseline Algorithm 1.Take company name. 2.Search Facebook/Twitter API using it. 3.Return first result from each. 1.Take company name. 2.Search Facebook/Twitter API using it. 3.Return first result from each. 8

9. Baseline Performance 9

10. Individual Network Approach 10

11. New Approach Score profiles based on  Edit Distance  Company Name – Username  Company Name – Display Name  Relative Popularity Score profiles based on  Edit Distance  Company Name – Username  Company Name – Display Name  Relative Popularity 11

12. 12 Display Name Username

13. New Approach Score profiles based on  Edit Distance  Company Name – Username  Company Name – Display Name  Relative Popularity Score profiles based on  Edit Distance  Company Name – Username  Company Name – Display Name  Relative Popularity 13

14. Scoring 14

15. Best Performing Combination 15

16. Machine Learning Experiments 16

17. Freebase Ground Truth 397,071 Business Operations1,422 with a social media presence917 with Facebook, 687 with Twitter598 with both553 with valid profiles 17

18. Training Set 553 Correct 553 Incorrect 1106 Total 18

19. Cross Validation Results ClassifierTest | TrainTrain | Test Linear Regression0.7340.707 Gaussian Naïve Bayes0.9720.956 Multinomial Naïve Bayes0.5110.506 Bernoulli Naïve Bayes0.7200.701 Decision Tree0.9540.935 19

20. Next Steps  Improve training set: provide harder examples 20

21. Next Steps  Improve training set: provide harder examples  Incorporate more profile data  Improve training set: provide harder examples  Incorporate more profile data 21

22. Next Steps  Improve training set: provide harder examples  Incorporate more profile data  Build system around classifiers  Improve training set: provide harder examples  Incorporate more profile data  Build system around classifiers 22

23. Agenda 23 Introduction Objective Contrast to Existing Work Work Done Baseline System Individual Network Approach Machine Learning Experiments Next Steps, Q&A

24. 24