This document provides an overview of a presentation on social media research opportunities. It discusses (1) the speaker's background and research interests, (2) statistics on popular social media platforms like Facebook and Twitter, (3) how social media is an emerging research field with large datasets, and (4) examples of academic research projects that have utilized social media data including recommender systems, cross-lingual search, and entity recognition tools. The document encourages researchers to take advantage of openly available social media data and application programming interfaces to conduct innovative studies on real-world problems.

1. Social Media DataSet
UPV, Aplicaciones de la Lingüística Computacional
Abril 22nd, 2010
http://www.slideshare.com/jccortizo

2. José Carlos Cortizo
Twitter: @josek_net

3. Goal
‣ Understand the possibilities of Social
Media for Research

4. Index
‣ About the Speaker
‣ Research at UEM
‣ SGP, Wipley, BrainSins
‣ Social Media statistics
‣ Social Media as research field
‣ Applications
‣ Academic research vs Enterprise

5. José Carlos Cortizo
10 R&P projects, 4 workshops, 20 papers
NLP, DM
consultancy
2004 2005 2006 2007 2008 2009 2010

6. My Company
SGP
Videogamers Social Network
2.600 reg. users, ~10K monthly users
SaaS software for SM & e-C
RecSys, Social Search...

7. SGP Funding
• €350K from CDTI (PID project)
• €100K from FFF
• still looking for extra €100K from BA

8. Web 2.0 vs.
Social Web

9. What’s Web 2.0?

10. An evolution (of) and
(based on) the Web...

11. ...focused on users
http://www.flickr.com/photos/cayusa/431036565

12. Concept introduced by
“Tim O’Reilly” in 2004
http://www.flickr.com/photos/thomashawk/153656919/

13. Users own the
Information
They should manage
the information

14. It’s not a technology,
it’s a philosophical
concept

15. Wikis (and Wikipedia),
blogs, etc.

16. Evolution of the Web

17. Concepts

18. AJAX
• Web development techniques
empowering Web 2.0
• Much developers misunderstand the
concept of Web 2.0 and think about
AJAX

19. Social Web
• Describe how people socialize with
each othe throughout the WWW
• 2 descriptions
• Web 2.0
• Proposal for a future network similar
to WWW

20. Social Media
• Media designed to be disseminated
through Social interaction
• Internet forums, blogs, microblogging,
wikis, podcasts, social networks, etc.
• More info: http://www.slideshare.net/jccortizo/taller-redes-
sociales-presentation

21. Social Media
Statistics

22. Facebook

23. Facebook stats.
• > 400M. active users
• 50% log in to FB in any given day
• > 35M u. update their status each day
• > 60M. status updates per day
• 3 billion photos uploaded each month
• 5 billion contents shared each week

24. Facebook stats.
• Avg. user has 130 friends on FB
• Avg. user sends 8 friend req. per month
• Avg. user spends > 55 min. per day
• > 70% FB users are outside USA
• > 500K applications

25. Facebook stats.
• > 60M FB users use FB Connect on
external websites
• > 100M accessing FB though mobile
• > 200 mobile operators in 60 cuntries
deploying/promoting FB mobile
products
http://www.facebook.com/press/info.php?statistics

26. Twitter

27. Twitter stats.
• > 105M. registered users
• 300K users sign up every day
• > 180M. unique visitors per month
• 75% traffic come from 3rd party apps.
• > 600M search queries on Twitter/day
• 37% of active users use mobile
http://www.readwriteweb.com/archives/just_the_facts_statistics_from_twitter_chirp.php

28. Facts

29. SM is the New Web
• Facebook traffic tops Google (for USA)
• FB > 7% of US traffic
• March 2010
• http://money.cnn.com/2010/03/16/technology/
facebook_most_visited/

30. SM envisioning the
Future
• Mobile Web
• Search
• Real-time search
• Social search
• Online identity
http://www.madrimasd.org/blogs/sistemas_inteligentes/2009/01/19/111413

31. Mobile Web
• No real mobile web until Social Media
• 25% FB users and 37% Twitter users
accesing from mobile devices
• Trend: more mobile web users than
“regular” ones within the next 5 years
[1]
[1] J. C. Cortizo, L. I. Diaz, F. Carrero, B. Monsalve, “On the Future of Mobile Phones as the Heart of Community Built Databases” to appear in 2011

32. Real-Time Search

33. Social Search

34. Online Identity
• User identity is a real business
• Facebook Connect, OAuth, OpenID...

35. Social Media as
Research Field

36. We need Data
• We need data to validate our research
• Why use “non-real”/small/”non-
relevant”/old-fashioned datasets
• UCI
• Reuters
• ...

37. SM, Huge DataSet
• Billions users
• Billions contents
• Textual, Multimedia (image, videos,
etc.)
• Billions of connections
• Behaviors, preferences, trends...

38. SM Openness
• It’s easy to get data from SM
• SM based datasets
• Developers APIS
• Spidering the Web

39. Available DataSets
• Social Tagging (CiteULike, Bibsonomy,
MovieLens, Delicious, Flickr, Last.FM...)
• http://kmi.tugraz.at/staff/markus/datasets/
• Yahoo! Firehose (750K ratings/day, 8K
reviews/day, 150K comments/day,
status updates, Flickr, Delicious...)
• http://developer.yahoo.net/blog/archives/
2010/04/yahoo_updates_firehose.html

40. Available DataSets
• MySpace data (real-time data,
multimedia content, ...)
• http://blog.infochimps.org/2010/03/12/
announcing-bulk-redistribution-of-
myspace-data/
• Spinn3r Blog Dataset/JDPA Sentiment
Corpus
• http://www.icwsm.org/data/

41. ...and more
• http://delicious.com/pskomoroch/
dataset

42. Key Benefits
• There’s a lot of data on SM
• It’s fun!
• You can work on a real-real domain
• ¿Make (real) money with your
research?

43. Where to publish?
• ICSWM: AAAI Conference on Weblogs
and Social Media
• MSM/SMUC: Workshop on Search and
Mining User generated Contents
• WWW: 4 Social Networks sessions +
other 15 S.M. related papers
• ACM RecSys + Social Web workshop

44. Where to publish?
• ICSWM: AAAI Conference on Weblogs
and Social Media
• MSM/SMUC: Workshop on Search and
Mining User generated Contents
• WWW: 4 Social Networks sessions +
other 15 S.M. related papers
• ACM RecSys + Social Web workshop

45. Where to publish?
• Any other ‘typical’ conference from
your research area
• Social web/search/mining/networks
analysis workshops on almost any
relevant conference

46. Other Research Uses
Twitter Lists

47. Other Research Uses
Twitter Searches

48. Other Research Uses
Twitter Users

49. Other Research Uses
Blogs!

50. Other Research Uses

51. Other Research Uses

52. • Don’t wait ‘till the conferences to know
about advances
• Follow interesting researchers through
Twitter and their blogs
• Peer-reviewing sucks!
• You can learn even more from failed
attempts, or work in progress
• Open your mind...

53. Some Applications

54. Buzzer: Twitter
RecSys

55. Buzzer
• O. Phelan (@phelo), K. McCarthy, B.
Smith, “Using Twitter to Recommend
Real-Time Topical News”, ACM
RecSys 2009
• Goal: News Recommendation
• Not using Reuters or similar datasets

56. Buzzer

57. Buzzer

58. Why use Twitter?
• “Typical” news sites are boring
• You’ll get compared to Google News
• You’re innovating just by use Twitter
• You’ll benefit from Twitter hype
• You get a real and interesting system
to deploy on real conditions

59. FlickrBabel: Multilingual
multimedia search

60. Machine Translation
• An open problem
• But actual state-of-the-art enough for
some applications

61. Idea
• Do we need a Spanish Metamap?
• F. Carrero, J. C. Cortizo, J. M. Gomez, M.
de Buenaga “In the Development of a
Spanish Metamap”, CIKM 2008
• F. Carrero, J. C. Cortizo, J. M. Gomez,
“Testing Concept Indexing in Crosslingual
Medical Text Classification”, ICDIM 2008

62. Idea

63. Idea

64. Idea
• Results show that using Google
Translate is just enough for
“simulating” a Spanish Metamap
• for classification purposes

65. Extending to SM
• We applied the same idea to
FlickrBabel

66. Extending to SM
• We applied the same idea to
FlickrBabel
• Search images from Flickr
• Babxel also searchs on YouTube
• Expands the query and improves the
recall

67. FlickrBabel

68. FlickrBabel
• We got a lot of buzz/users from
• Mashable
• Loogic
• WwwhatsNew
• And thousand more blogs/sites

69. evri: entity based search
engine (+ API)

71. evri
• Not a typical search engine
• Real-time
• Semantic
• Entity recognition
• Opinion Mining

72. Offers a great API

73. What’s good
• They integrate a lot of technologies
from the state-of-the-art on NLP/IR into
something usable
• The API can be used to develop evri
based products and applications
• If you have a good technology, build a
good product/service around it

74. Entity recognition

75. Recommendations

76. Sentiment Analysis

77. No good enough?
• There isn’t “no good enough”
technologies
• There are useful or not useful products/
services
• Show your technology to the world,
they’d be the best ‘reviewers’

78. Academic vs
Enterprise

79. · Too idealist Too pragmatic ·
· ‘Fantasy?’ world ‘Real?’ world ·
· Too many assumptions Too little assumptions ·
· Research ‘Innovate’ ·
· Guided by public funds Guided by revenues ·
· Non-applicable Cuts innovation ·

80. · Too idealist
There’s a lot of pragmatic ·
Too
opportunities ‘Real?’ world ·
· ‘Fantasy?’ world
· Too many assumptions Too little assumptions ·
· Research in the middle!! ‘Innovate’ ·
· Guided by public funds Guided by revenues ·
· Non-applicable Cuts innovation ·

81. Entrepreneurship: the
Research Way

82. • Choose a real world problem (take care of
data availability, competitors and utility)
• Develop a great technology
• Test in a lab environment and publish
• Develop a prototype and grant access to
beta testers

83. • Analyze the new results
• Write a (presentation based) business plan
• Get money from FFF
• Develop your product (out of beta)
• Get some clients/users

84. • Write a full business plan
• You can get help from your University/
other institutions
• Get more funding from BA’s and VC’s
• Hire the best coders/employees you can
get
• Monetize your product/service

85. • And don’t stop researching and innovating

86. Jo
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Ca
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BY, 2010 Pé
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