This document provides an overview and schedule for a workshop on analyzing and visualizing geo data from social media sources. It discusses extracting geo data from microformats, Twitter, LinkedIn, and Facebook. Hands-on exercises are demonstrated for analyzing Twitter data using Python scripts and visualizing results. Clustering approaches for grouping geo data are also introduced.

1. Mining the Geo Needles in
the Social Haystack
(Where 2.0, 2011)
Matthew A. Russell
http://linkedin.com/in/ptwobrussell
@ptwobrussell

2. About Me
• VP of Engineering @ Digital Reasoning Systems
• Principal @ Zaffra
• Author of Mining the Social Web et al.
• Triathlete-in-training
@SocialWebMining
2

3. Objectives
• Orientation to geo data in the social web space
• Hands-on exercises for analyzing/visualizing geo data
• Whet your appetite and send you away motivated and with useful
tools/insight
3

4. Approximate Schedule
• Microformats: 10 minutes
• Twitter: 15 minutes
• LinkedIn: 15 minutes
• Facebook: 15 minutes
• Text-mining: 15 minutes
• General Q&A (time-permitting)
4

5. Development
• Your local machine
• Python version 2.{6,7}
• Recommend Windows users try ActivePython
• We'll handle the rest along the way
5

6. Microformats
Agile Data Solutions

7. Microformats
• My definition: "conventions for unambiguously including structured
data into web pages in an entirely value-added way" (MTSW, p19)
• Bookmark and browse: http://microformats.org
• Examples:
• geo, hCard, hEvent, hResume, XFN
7

8. geo
<!-- Download MTSW pp 30-34 from XXX -->
<!-- The multiple class approach -->
<span style="display: none" class="geo">
<span class="latitude">36.166</span>
<span class="longitude">-86.784</span>
</span>
<!-- When used as one class, the separator must be a semicolon -->
<span style="display: none" class="geo">36.166; -86.784</span>
8

9. Exercise!
• View source @ http://en.wikipedia.org/wiki/List_of_U.S._national_parks
• Use http://microform.at to extract the geo data as KML
• http://microform.at/?type=geo&url=http%3A%2F%2Fen.wikipedia.org
%2Fwiki%2FList_of_U.S._national_parks
• Try pasting this URL into Google Maps and see what happens
9

10. Exercise Results
• Feel free to hack on the KML
• http://code.google.com/apis/kml/documentation/
• Google Earth can be fun too
• But you already knew that
• We'll see it later...
10

11. Twitter
Agile Data Solutions

12. Twitter Data
• There's geo data in the user profile
• And in tweets...
• ...if the user enabled it in their prefs
• And even in the 140 chars of the tweet itself
12

13. A Tweet as JSON
{
"user" : {
"name" : "Matthew Russell",
"description" : "Author of Mining the Social Web; International Sex Symbol",
"location" : "Franklin, TN",
"screen_name" : "ptwobrussell",
...
},
"geo" : { "type" : "Point", "coordinates" : [36.166, 86.784]},
"text" : "Franklin, TN is the best small town in the whole wide world #WIN",
...
}
13

14. Exercise!
• In your browser, try accessing this URL:
http://api.twitter.com/1/users/show.json?screen_name=ptwobrussell
• In a terminal with Python, try it programatically:
$ sudo easy_install twitter # 1.6.1 is the current
$ python
>>> import twitter
>>> t = twitter.Twitter()
>>> user = t.users.show(screen_name='ptwobrussell')
>>> import json
>>> print json.dumps(user, indent=2)
14

15. Recipe #21
• Geocode locations in profiles:
• https://github.com/ptwobrussell/Recipes-for-Mining-Twitter/blob/
master/recipe__geocode_profile_locations.py
• Recipe #21 from 21 Recipes for Mining Twitter
15

16. Sample Results
<?xml version="1.0" encoding="UTF-8"?>
<kml xmlns="http://earth.google.com/kml/2.0">
<Folder>
<name>Geocoded profiles for Twitterers showing up in search results for ... </name>
<Placemark>
<Style>
<LineStyle>
<color>cc0000ff</color>
<width>5.0</width>
</LineStyle>
</Style>
<name>Paris</name>
<Point>
<coordinates>2.3509871,48.8566667,0</coordinates>
</Point>
</Placemark>
...
</kml> 16

17. Recipe #20
• Visualizing results with a Dorling Cartogram:
• https://github.com/ptwobrussell/Recipes-for-Mining-Twitter/blob/
master/recipe__dorling_cartogram.py
• Recipe #20 from 21 Recipes for Mining Twitter
17

18. Sample Results
18

19. Recipe #22 (?!?)
• Extracting "geo" fields from a batch of search results
• https://github.com/ptwobrussell/Recipes-for-Mining-Twitter/blob/
master/recipe__geocode_tweets.py
• Not in current edition of 21 Recipes for Mining Twitter
• Just checked in especially for you
19

20. Sample Results
• Unfortunately (???), "geo" data for
[None, None, None, None, None, None, None, None, None, None,
tweets seems really scarce None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
• Varies according to a particular None, None, {u'type': u'Point', u'coordinates':
[32.802900000000001, -96.828100000000006]}, {u'type':
u'Point', u'coordinates': [33.793300000000002, -117.852]},
None, None, None, None, None, None, None, None, None, None,
user's privacy mindset? None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, {u'type': u'Point', u'coordinates':
[35.512099999999997, -97.631299999999996]}, None, None,
• Examining only Twitter users who None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
enable "geo" would be interesting None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
None, None, None, None, None, None, None, None, None, None,
in and of itself 20
None]

21. Mining the 140 Characters
• Not a trivial exercise
• Mining natural language data is hard
• Mining bastardized natural language data is even harder
• We'll look at mining natural language data later
21

22. Fun Possibilities
#JustinBieber #TeaParty
22

23. Oh, and by the way...
23

24. OAuth 1.0a - Now
import twitter
from twitter.oauth_dance import oauth_dance
# Get these from http://dev.twitter.com/apps/new
consumer_key, consumer_secret = 'key', 'secret'
(oauth_token, oauth_token_secret) = oauth_dance('MiningTheSocialWeb',
consumer_key, consumer_secret)
auth=twitter.oauth.OAuth(oauth_token, oauth_token_secret,
consumer_key, consumer_secret)
t = twitter.Twitter(domain='api.twitter.com', auth=auth)

25. OAuth 2.0 - "Soon"
+----------+ Client Identifier +---------------+
| -+----(A)--- & Redirect URI ------>| |
| End-user | | Authorization |
| at |<---(B)-- User authenticates --->| Server |
| Browser | | |
| -+----(C)-- Authorization Code ---<| |
+-|----|---+ +---------------+
| | ^ v
(A) (C) | |
| | | |
^ v | |
+---------+ | |
| |>---(D)-- Client Credentials, --------' |
| Web | Authorization Code, |
| Client | & Redirect URI |
| | |
| |<---(E)----- Access Token -------------------'
+---------+ (w/ Optional Refresh Token)
See http://tools.ietf.org/html/draft-ietf-oauth-v2-10#section-1.4.1

26. LinkedIn
Agile Data Solutions

27. LinkedIn Data
• Coarsely grained geo data is available in user profiles
• "Greater Nashville Area", "San Francisco Bay", etc.
• Most geocoders don't seem to recognize these names...
• No geocoordinates! (Yet???)
• Mitigation approach: (1) transform/normalize and then (2) geocode
27

28. Exercise!
• Get an API key at http://code.google.com/apis/maps/signup.html
$ easy_install geopy
$ python
>>> import geopy
>>> g = geopy.geocoders.Google(GOOGLE_MAPS_API_KEY)
>>> results = g.geocode("Nashville", exactly_one=False)
>>> for r in results:
... print r # (u'Nashville, TN, USA', (36.165889, -86.784443))
• See also https://github.com/ptwobrussell/Recipes-for-Mining-Twitter/blob/
master/etc/geocoding_pattern.py 28

29. Diving Deeper
• Example 6-14 from MTSW (pp194-195) works though an extended example
and dumps KML output that includes clustered output
• See http://github.com/ptwobrussell/Mining-the-Social-Web/python_code/
linkedin__geocode.py
29

30. Clustering
• First half of MTSW Chapter 6 (pp167-188) provides a good/detailed intro
• Think of clustering as "approximate matching"
• The task of grouping items together according to a similarity metric
• It's among the most useful algorithmic techniques in all of data mining
• The catch: It's a hard problem.
• What do you name the clusters once you've created them?
30

31. Example Output
31

32. Better Data Exploration
32

33. Clustering Approaches
• Agglomerative (hierarchical)
• Greedy
• Approximate
• k-means
33

34. k-Means Algorithm
1. Randomly pick k points in the data space as initial values that will be used to compute the
k clusters: K1, K2, ..., Kk.
2. Assign each of the n points to a cluster by finding the nearest Kn—effectively creating
k clusters and requiring k*n comparisons.
3. For each of the k clusters, calculate the centroid (the mean of the cluster) and reassign
its Ki value to be that value. (Hence, you’re computing “k-means” during each iteration of the
algorithm.)
4. Repeat steps 2–3 until the members of the clusters do not change between iterations.
Generally speaking, relatively few iterations are required for convergence.
Let's try it: http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html
34

35. Step 0 (init)
35

36. Step 1
36

37. Step 2
37

38. Step 3
38

39. Step 4
39

40. Step 5
40

41. Step 6
41

42. Step 7
42

43. Step 8
43

44. Step 9 (done)
44

45. k-Means Applied
45

46. Facebook
Agile Data Solutions

47. Facebook Data
• Ridiculous amounts of data (all kinds) is available via the FB Platform
• Current location, hometown, "checkins"
• Access to the FB platform data is relatively painless:
• Social Graph: http://developers.facebook.com/docs/reference/api/
• FQL: http://developers.facebook.com/docs/reference/fql/
47

48. FQL Checkins
• See http://developers.facebook.com/docs/reference/fql/checkin/
48

49. FQL Connections
• See http://developers.facebook.com/docs/reference/fql/connection/
49

50. Sample FQL
• An excerpt from MTSW Example 9-18 (pp306-308) conveys the gist:
fql = FQL(ACCESS_TOKEN)
q=
"""select name, current_location, hometown_location
from user
where uid in
(select target_id
from connection
where source_id = me() and target_type = 'user')"""
results = fql.query(q)
50

51. Example "App"
• Basic idea is simple
• You already have the tools to
geocode and plot on a map...
• See also: http://answers.oreilly.com/
topic/2555-a-data-driven-game-
using-facebook-data/
51

52. FB Platform Demo
• Mininal sample app at http://miningthesocialweb.appspot.com
• Source is at http://github.com/ptwobrussell/Mining-the-Social-Web/
web_code/facebook_gae_demo_app
52

53. Text Mining
Agile Data Solutions

54. References
• MTSW Chapter 7 (Google Buzz: TF-IDF, Cosine Similarity, and Collocations)
• MTSW Chapter 8 (Blogs et al.: Natural Language Processing and Beyond)
54

55. "Legacy" NLP
• "Legacy" => Classic Information Retrieval (IR) techniques
• Often (but not always) uses a "bag of words" model
• tf-idf metric is usually the root of the core strategy
• Variations on cosine similarity are often the fruition
• Additional higher order analytics are possible, but inevitably
cannot be optimal for deep semantic analysis
• Virtually every A-list search engine has started here
55

56. A Vector Space
56

57. How might you discover locations from text
using "legacy" techniques?
57

58. Some possibilities
•Combinations of language dependent "hacks"
•n-gram detection/examination
•bigrams, trigrams, etc.
•"Proper Case" hints
•"Chipotle Mexican Grill"
•prepositional phrase cues
•"in the garden", "at the store"
•Gazetteers
•lists of "well-known" locations like "Statue of Liberty"
58

59. "Modern" NLP Pipeline
•A deeper "understanding" the data is much harder
•End of Sentence (EOS) Detection
•Tokenization
•Part-of-Speech Tagging
•Chunking
•Anaphora Resolution
•Extraction
•Entity Resolution
•Blending in "legacy" IR techniques can be very helpful in reducing noise
59

60. Entity Interactions
60

61. Quality Metrics
• Precision = TP/(TP+FP)
• Recall = TP/(TP+FN)
• F1 = (2*P*R)/(P+R)
61

62. Exercise!
• Get a webpage:
• curl http://example.com/foo.html
• Extract the text:
• curl -d @foo.html "http://www.datasciencetoolkit.org/html2story" > foo.json
• Extract the locations:
• curl -d @foo.json "http://www.datasciencetoolkit.org/text2places"
• NOTE: Windows users can work directly at http://www.datasciencetoolkit.org
62

63. Tools to Investigate
• NLTK - http://nltk.org
• Data Science Toolkit - http://www.datasciencetoolkit.org
• WordNet - http://wordnet.princeton.edu/
63

64. Q&A
Agile Data Solutions

65. The End
Agile Data Solutions