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Linking Organizational Social Networking ProfilesPROJECT ID: H0791030JEROME CHENG ZHI KAI (A0080860H)

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Example: Holiday InnTWITTER FACEBOOK

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Motivation: Individuals

• Want to find profiles, but no one place has them

• Sometimes on company websites, but:• No standardized location• Not all companies bother

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Motivation: Organizations

• Track competitor’s use of social media

• Find imposter profiles

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Problem Definition

System

Social Profiles

Organization Name

Official

Affiliate

Unrelated

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Related Work

• Focused on deduplication for individuals

• Relevant: profile characteristics focused on

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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)

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Related Work: Created Content

• Identifying Users Across Social Tagging Systems (Iofciu, Fankhauser, Abel & Bischoff, 2011)

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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

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Classifier Choice

• Evaluated scikit-learn’s:• Decision Tree• Naïve Bayes• Support Vector• Logistic Regression• Random Forest

• Features aren’t independent – trees are well-suited

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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

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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

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Feature Breakdown: Description

• Occurrences of Query

• Cosine Similarity• Query and Description• Duckduckgo Description and Profile Description

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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

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Evaluation: Ground Truth Creation

1. Retrieved organizations from Freebase

2. Searched for profiles on Twitter/Facebook

3. Manually labelled as official/affiliate/unrelated

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Evaluation: Ground Truth Breakdown

TWITTER CLASSES

Official; 232; 7%

Affiliate; 675; 20%

Unrelated; 2474; 73%

FACEBOOK CLASSES

Official; 146; 4%Affiliate; 491; 14%

Unrelated; 2776; 81%

3381 labels 3413 labels

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Evaluation: Process

• Mainly concerned with official and affiliate classes• Not interested in unrelated class

• Modified 10-fold Cross Validation

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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 system2. Count number of correct results

4. Calculate precision/recall/F1 from counts

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Evaluation: Baseline

• Normalised Edit Distance: Username/Display Name and Query

• Emulates searching networks manually without examining profile in detail

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Results & Discussion: Twitter

F1 Precision Recall0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0.559

0.716

0.458

0.862

0.947

0.791

Official

Baseline Final

F1 Precision Recall0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0.7130.750

0.559

0.905 0.884 0.862

Affiliate

Baseline Final

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Results & Discussion: Facebook

F1 Precision Recall0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0.7500.792

0.711

0.8840.945

0.830

Official

Baseline Final

F1 Precision Recall0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0.559

0.744

0.480

0.8620.816

0.639

Affiliate

Baseline Final

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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

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Discussion: Facebook Characteristics

• Many profile types: people, pages, places, etc.

• Finding official pages is simplified

• But: finding affiliates requires more effort

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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

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Limitations

• Ground truth proportions: expand and/or balance

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Limitations

• Ground truth proportions: expand and/or balance

• Limited number of profiles retrieved for classification

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Future Work

• Support additional networks

• Examine post content

• “Preferential” classification