Linking Organizational Social Networking Profiles PROJECT ID: H JEROME CHENG ZHI KAI (A H ) 1

Example: Holiday Inn TWITTERFACEBOOK 2

Motivation: Individuals Want to find profiles, but no one place has them Sometimes on company websites, but: No standardized location Not all companies bother 3

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Motivation: Organizations Track competitor’s use of social media Find imposter profiles 6

Problem Definition 7 System Social Profiles Organization Name Official Affiliate Unrelated

Related Work Focused on deduplication for individuals Relevant: profile characteristics focused on 8

Related Work: Usernames Connecting Corresponding Identities across Communities (Zafarani & Liu, 2009) Connecting users across social media sites: a behavioral- modeling approach (Zafarani & Liu, 2013) Studying User Footprints in Different Online Social Networks (Malhotra et al., 2012) 9

Related Work: Created Content Identifying Users Across Social Tagging Systems (Iofciu, Fankhauser, Abel & Bischoff, 2011) 10

Methodology: System Design 1.Input: organization’s name (query) 2.Search Facebook/Twitter APIs, retrieve profiles 3.Convert profiles into feature vectors 4.Classify profile-as-vectors 11

Classifier Choice Evaluated scikit-learn’s: Decision Tree Naïve Bayes Support Vector Logistic Regression Random Forest Features aren’t independent – trees are well-suited 12

Feature Breakdown: Name-based Normalized Edit Distance Query to Username Query to Display Name Edit Distance Query to Username Query to Display Name Length of Query Length of Username Length of Display Name 13

Feature Breakdown: Name-based Quirks Need to handle abbreviations, stopwords Citigroup versus Citi, General Motors versus GM Take two edit distances: original string, processed string Use better scoring of the two 14

Feature Breakdown: Description Occurrences of Query Cosine Similarity Query and Description Duckduckgo Description and Profile Description 15

Feature Breakdown: Language Models Construct Bigram Language Model for: Official profile descriptions Affiliate profile descriptions Unrelated profile descriptions Probability that candidate description belongs to each 16

Evaluation: Ground Truth Creation 17 1.Retrieved organizations from Freebase 2.Searched for profiles on Twitter/Facebook 3.Manually labelled as official/affiliate/unrelated

Evaluation: Ground Truth Breakdown TWITTER CLASSESFACEBOOK CLASSES labels3413 labels

Evaluation: Process Mainly concerned with official and affiliate classes Not interested in unrelated class Modified 10-fold Cross Validation 19

Evaluation: Modified Cross Validation 1.Generate folds as per normal 2.Train classifier on training set as per normal 3.For each affiliate/official profile in test set: 1. Input organization’s name to system 2. Count number of correct results 4.Calculate precision/recall/F1 from counts 20

Evaluation: Baseline Normalised Edit Distance: Username/Display Name and Query Emulates searching networks manually without examining profile in detail 21

Results & Discussion: Twitter 22

Results & Discussion: Facebook 23

Discussion Baseline performs well for official class on Facebook Username and display name alone are good indicators for this class Other features still help, but not as much 24

Discussion: Facebook Characteristics Many profile types: people, pages, places, etc. Finding official pages is simplified But: finding affiliates requires more effort 25

Discussion: Facebook Characteristics Facebook doesn’t require a “username” be specified for pages Will just use an ID instead Auto-generated pages also only have IDs, use name from Wikipedia/other sources 26

Limitations Ground truth proportions: expand and/or balance 27

Limitations Ground truth proportions: expand and/or balance Limited number of profiles retrieved for classification 28

Future Work Support additional networks Examine post content “Preferential” classification 29