1. Linking Organizational Social Networking Profiles Research Wrap-Up – 28 August 2015 1

2. Develop a system to find an organization’s profiles across different social networks. Objecti ve    2

3. Brand s Regiona l Affiliates Affiliate Profiles 3

4. System Overvie w Organization Name Official Affiliate Unrelate d 4

5. Official Profiles representing the company as a whole. e.g. @Microsoft, @Dell (respectively) Affiliate Profiles representing a brand or regional affiliate. e.g. @Surface, @Windows, @MicrosoftAsia Unrelat ed Profiles that aren’t run by the company itself. Includes employees, other companies. 5

6. Introduction Implementa tion Evaluation Results/Discus sion Future? 6

7. Input Processing Query GET /company/Microsoft Corporation Profile Acquisition Twitter/Faceboo k Search API DuckDuckGo Instant Answers API Processed Query e.g. “Microsoft” Profile Conversion Profile Classification Twitter/Facebook Profiles Feature Vectors Labelled Profiles json Pipeline 7

8. 8

9. Input Processing Query GET /company/Microsoft Corporation Profile Acquisition Twitter/Faceboo k Search API DuckDuckGo Instant Answers API Processed Query e.g. “Microsoft” Profile Conversion Profile Classification Twitter/Facebook Profiles Feature Vectors Labelled Profiles json Pipeline 9

10. Input Processing Query DuckDuckGo Instant Answers API, which gives a “topic summary”. Take the name from that summary. 10

11. Profile Acquisition Query Twitter/Facebook’s search API and retrieve 20 candidate profiles. 11

12. Name- based (5) N1: Normalized Edit Distance: Query to Username N2: Normalized Edit Distance: Query to Display Name N3: Length of Query N4: Length of Username N5: Length of Display Name Descripti on-based (3) D1: Occurrences of Query in Description D2: Cosine Similarity: Query and Description D3: Cosine Similarity: Profile Description and DuckDuckGo Description Languag e Model- based (6) LM1: “Official” Description LM Probability LM2: “Affiliate” Description LM Probability LM3: “Unrelated” Description LM Probability LM4: “Official” Post LM Probability LM5: “Affiliate” Post LM Probability LM6: “Unrelated” Post LM Probability Profile Conversion - Features 12

13. Name-based Features N1 - Normalized Edit Distance: Query to Username N2 - Normalized Edit Distance: Query to Display Name N3 - Length of Query N4 - Length of Username N5 - Length of Display Name 0 when completely different, 1 when identical Username: GM Display Name: General Motors 13 Quir ks Abbreviations: GM versus General Motors Stopwords: “Corporation”, “Company”, etc. Imposters!

14. Description-based Features D1 - Occurrences of Query D2 - Cosine Similarity: Query and Description D3 - Cosine Similarity: DuckDuckGo Description and Profile Description 14

15. Language Model-based Features Probability that description/posts appear in each language model: Description LM1 - Official Profiles LM2- Affiliate Profiles LM3 - Unrelated Profiles Recent Posts LM4 - Official Profiles LM5 - Affiliate Profiles LM6 - Unrelated Profiles 15

16. 3381 labels from 228 organizations Twitter Labels 3403 labels from 216 organizations Facebook Labels Ground Truth Breakdown 16

17. Per-Fold Evaluation Process Official Profiles Affiliate Profiles 1. Training set is used to train the classifier. Classifier Unrelated Profiles 2. Test set is filtered for official and affiliate profiles. Official Profiles Affiliate Profiles Test Set 3. Obtain list of organizations that own these profiles. Official Profiles Affiliate Profiles Organization Names System 4. Names used to query system, results used to calculate performance. Organizatio n Names Classifier Classified Official Classified Affiliate Classified Unrelated 17

18. Baseli ne Simulates manually judging profiles by name alone. N1 - Normalized Edit Distance: Query to Username N2 - Normalized Edit Distance: Query to Display Name 18

19. 19 Official Affiliate Results - Twitter

20. 20 Official Affiliate Results - Facebook

21. 21 Profile Types Facebook has multiple profile types: people, pages, places, groups, etc. Twitter has just one: people. Affiliate s? Why don’t FB affiliates score as well? Page usernames are optional. /pages/Netflix- Latinoamérica/553454298124413 Display Name ID

22. 22 Profile Types Facebook has multiple profile types: people, pages, places, groups, etc. Twitter has just one: people. Affiliate s? Why don’t FB affiliates score as well? Page usernames are optional. /pages/Netflix- Latinoamérica/553454298124413 Display Name ID

23. 23 Affiliate s? Why don’t FB affiliates score as well? Page usernames are optional. /pages/Netflix- Latinoamérica/553454298124413 Display Name ID Auto-generated pages also follow the same pattern!

24. 24 Future ? Focus on affiliates – unique to the domain.

25. 25 Future ? Focus on affiliates – unique to the domain. Drill down into the various different types: (e.g.) outreach, regional, brand, business unit.

26. 26 Future ? Focus on affiliates – unique to the domain. Drill down into the various different types: (e.g.) outreach, regional, brand, business unit. Improve ground truth: crowd-source labels.

27. 27 Done Objective: develop a system to find an organization’s profiles across different social networks Used network-specific classifiers to do so Evaluated performance using modified cross- validation FUture Dive deeper into affiliates, which are unique to organizations