Collating Social Network Profiles. Objective 2 System.

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Presentation transcript:

Collating Social Network Profiles

Objective 2 System

Objective 3 Company Name System Social Network Profiles InputOutput

4 Record Linkage + Identity

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

Baseline System 6

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

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

Baseline Performance 9

Individual Network Approach 10

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 Display Name Username

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

Scoring 14

Best Performing Combination 15

Machine Learning Experiments 16

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

Training Set 553 Correct 553 Incorrect 1106 Total 18

Cross Validation Results ClassifierTest | TrainTrain | Test Linear Regression Gaussian Naïve Bayes Multinomial Naïve Bayes Bernoulli Naïve Bayes Decision Tree

Next Steps  Improve training set: provide harder examples 20

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

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

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

24