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Enhancing Social Network Profile Accuracy for Corporate Identification

This project aims to develop a system that collates and analyzes social network profiles (Twitter, Facebook, Google+) of the top 70 Fortune 500 companies based on a baseline algorithm. By utilizing company names to search social media APIs, the system retrieves profiles and scores them using an innovative approach that considers edit distance, username-display name correlation, and relative popularity. The ultimate goal is to improve training sets by including more complex examples and integrating diverse profile data, thus refining the identity verification process across networks.

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Enhancing Social Network Profile Accuracy for Corporate Identification

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  1. Collating Social Network Profiles

  2. Objective System <Twitter Profile, Facebook Profile, G+ Profile, …> <Twitter Profile, Facebook Profile, G+ Profile, …> <Company Name>

  3. Objective System Input Output <Twitter Profile, Facebook Profile, G+ Profile, …> Social Network Profiles Company Name

  4. Record Linkage + Identity

  5. Agenda

  6. Baseline System

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

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

  9. Baseline Performance

  10. Individual Network Approach

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

  12. Display Name Username

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

  14. Scoring Edit Distance Score: Popularity Score:

  15. Best Performing Combination

  16. Machine Learning Experiments

  17. Freebase Ground Truth

  18. Training Set

  19. Cross Validation Results

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

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

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

  23. Agenda

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