MDS 2011 Presentation: An Unsupervised Approach to Discovering and Disambiguating Social Media Profiles

An Unsupervised Approach to Discovering and Disambiguating Social Media Profiles
Mining Data Semantics Workshop 2011
Carlton Northern
Old Dominion University
8/25/2011
1

Background
Digital Preservation
How are students using social media as a digital preservation strategy?
Evaluating Personal Archiving Strategies for Internet-based Information - Marshall, McCown, Nelson http://www.cs.odu.edu/~mln/pubs/archiving-2007/eval-personal-arch-strat-archiving07.pdf
2

Goal
Ascertain the set of social media profiles for ODU CS students.
{
}
...
3

4
What's out there already?

5
Intelius

Wink / my life
6

Google
7

Requirements and Assumptions
Approach must be automated - no human interaction except for search query consisting of:
location
organization
profession/education domain.
Achieve precision 0.7 or higher and f-measure 0.5 or higher comparable to a human level of the same activity
Must find profiles not indexed by search engines
Can use any means available including using search engines, page scraping, web service APIs, etc.
Only publicly declared identities; do not expose obfuscated identities
e.g., “Bruce Wayne“ -> “Batman"
Find profiles from 25 pre-defined sites (next slide)
Approach must be extensible,
i.e. new social media sites can be added with minimal changes.
8

Social Media Sites
9

Approach
10

11
Algorithm
Discovery Phase
Generate Usernames
Check Rapportive
Disambiguation Phase
Assign Points for Keywords, Email, Me and Friend Links
Check Google and Yahoo
Check Sites for Profiles
Check Sites For Profiles
Check Social Graph
Remove Duplicates
*Run multiple times

Discovery Phase
12

Starting Information
Given:
Full name, i.e. Carlton Northern
CS username, i.e. cnorther
CS email, i.e. cnorther@cs.odu.edu
.forward files -> carlton.northern@gmail.com
CS profile URI, i.e. http://www.cs.odu.edu/~cnorther
Inferred:
School affiliation, i.e. Old Dominion
Approximate location, i.e. Norfolk, Hampton Roads
Computer Science affiliation, i.e. software engineer
13

Username Generation
Generate usernames from full name derivatives, i.e. for “Carlton Northern” we have:
cnorthern
northernc
carlton.northern
carlton_northern
carlton-norther
14

Poll Sites
Issue HTTP GET to determine if a profile exists with a generated username
Create site templates for links:
http://www.facebook.com/’username here’
http://www.stumbleupon.com/stumbler/’username here’
https://picasaweb.google.com/’username here’
2016 students, 6 usernames, 25 sites = 302k requests
GET http://www.facebook.com/carlton.northern HTTP/1.1
If 200 accept response, profile exists, else it doesn’t.
Soft 404’s can be somewhat problematic but can be handled.
Some sites detect robots and will present a Captcha which is also problematic.
15

Run existing profile URLs through Google Social Graph to find “me” links.
16
Google’s Social Graph API

“Me” Links
“me” links are links in Friend of a Friend (FOAF) and XHTML Friends Network (XFN) that specify the same identity
For example, a me link from my CS profile page to twitter:
17
<html>
<head>
<title>Carlton Northern's CS Home Page</title>
</head>
<body>
stuff here ...
<a href=http://twitter.com/carltonnorthern rel=“me”>My Twitter</a>
</body>
</html>

Rapportive
Rapportive is a contacts relationship management (CRM) tool that sits on top of Gmail
Uses AJAX and JSON to serve up content to their Gmail widget.
Mined .forward files on the CS departmental server
Found only 24 email addresses out of 2016 students
Run CS and non CS email addresses through Rapportive’s not-so-public API to access their results.
Produced 15.9% of our truth set profile results with only 1.6% being unique to Rapportive
18

Google and Yahoo
Query Google and Yahoo using their respective APIs.
“carlton northern" AND norfolk
“carlton northern" AND “computer science"
“carlton northern" AND “old dominion“
“carlton northern” site:http://www.facebook.com
Geonames could be used to derive nearby cities to automatically form search queries
The same could be done with WordNet to derive profession or education terms
19

Google and Yahoo
Calls to Google and Yahoo need to be limited because of API restrictions.
Google restricts use to about 1,000 requests per hour
Furthermore, best results are in the first 1 – 8 positions of the result set
20

Disambiguation Phase
21

22
From a public Facebook profile you can (sometimes) get a persons full name, city/area, friends and picture

23
Personally Identifiable Information Poor Profile

Personally Identifiable Information Rich Profile
24

Point System
Simple point system:
Keyword matching
Link community structure analysis
Extraction of semantic and feature data from profiles
11 points is considered a validated profile.
Points can range from a total negative score to about 50.
25

Keyword Matching
1 point for weak indicators
1 word terms like “programmer” or “student”
4 points for stronger indicators
2 or more words terms like “computer science” or “software engineer”
7 points for very strong indicators
locations i.e. “norfolk” or “portsmouth”
Localized advertisements can be problematic
2 points for first name or given name
4 points for last name
26

Name Matching
Facebook, Linkedin, Google, and Twitter, use real names so:
2 points for a first name or diminutive/nickname
5 points for a last name
Subtract 21 points if neither a nickname or diminutive and a last name are found
Watch out for diminutive/nicknames!
http://code.google.com/p/nickname-and-diminutive-names-lookup/
Linkedin in provides location
add or subtract 7 points
27

Link Community Structure Analysis
Retrieve all links in a page and see if they point to other validated profiles in the data set, if so, assign 5 points
28
Validated Profile
Not-Validated Profile
Assign 5 points to Michael’s Twitter profile

Me Links and Email Matching
10 points if a profile is found from Rapportive
10 points if a profile has a me link from an already validated profile
29
Validated Profile
Not-Validated Profile
Assign 10 points to Carlton’s Twitter profile

Experiment
30

Dataset
2016 students from our departmental server
142 graduate
1874 undergraduate
Generated 9GB worth of data
Truth set: 20 graduate students and 2 professors from our research group Web Science and Digital Libraries
Use information retrieval metrics of precision, recalland f-measure to assess our truth set
31

Truth Set Results Summary
32

Social Media Web Site Results
33

34
Whole Set Service Graph

36
Truth Set User Graph

37
Whole Set User Graph

39
Whole Set User Graph Without Blogger Links

40
Closeup

Future Work
Facial recognition
Better link community structure analysis
Perform quantitative social media digital preservation study
Remove social media sites that produced no or little results (unpopular) and add new ones (foursquare.com)?
41

Potential Impacts/Uses
Open source intelligence gathering
“Open source” as in publicly available information
Social media research
Measure the social health of an organization
42

Conclusions
Completely automated with the only human interaction being with the creation of the search query
Precision 0.863, recall .526, f-measure 0.632
The approach uses non-traditional search mechanisms to achieve it's goals
Only publicly available information was used
43

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Carlton Northern
carlton.northern@gmail.com
http://carlton-northern.com/

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MDS 2011 Presentation: An Unsupervised Approach to Discovering and Disambiguating Social Media Profiles

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MDS 2011 Presentation: An Unsupervised Approach to Discovering and Disambiguating Social Media Profiles — Presentation Transcript