1. Computational Social Science:
The Pros and Cons of 'Big Data’
Cliff Lampe
- School of Information, University of Michigan
May 20, 2013
Monday, May 20, 13

2. Cliff Lampe
School of Information
- associate professor
Social media
“Socio-technical systems”
Primarily a social scientist
Monday, May 20, 13

3. Samples of my research in this area
Effects of participation on Facebook
Information cascades on Twitter
User collaboration on Wikipedia
Discussion patterns in large-scale news sites
Coordination in massive online games
Information seeking on search engines vs. social media
Monday, May 20, 13

4. Interactions in social media leave communication
traces we can mine to understand social processes.
These compete with insights from surveys.
Monday, May 20, 13

5. Defining “Big Data”
“Big Data” is a rough categorization, a marketing
term, and a paradigm shift.
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6. Why “big data” has become
a big deal...
More devices collecting data
More data born digital
Easier/cheaper to store
Better processors
New skills / techniques
Insights have proven effective
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8. “Big Data” started in the
physical sciences
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9. Big Data is increasingly being
applied to social science questions
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10. What counts as “big”?
LHC: .001% of sensors lead
to 25 petabytes annually.
Wikipedia: 17 terabytes
Twitter: ~ 10 GB/day
How many observations
needed to count as “big”?
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11. ‘Big Data’ require multiple,
interlinked skills and tools.
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13. Challenges in “Big Data”
Capture
Curation
Storage
Search
Sharing
Transfer
Analysis
Visualization
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14. Social Media is often linked with
Big Data because it is the likeliest
source for human trace data.
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15. What is social media?
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16. Common characteristics
User generated content
Direct user-to-user interaction
Bundles of applications
More than Facebook and Twitter
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25. Social media cover a wide
variety of sites.
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26. Trends in social media use
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30. Social media skill
Nearly 1 million people join Facebook every week
People spend on average 16 hours a month on
Facebook
There are about 250 million Tweets per day
People upload 3000 pictures to Flickr every minute
Wikipedia has 17 million articles by 91,000 editors
YouTube has 490 million unique visitors per month
Google + reached 10 million users in 16 days
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31. The social media landscape
is constantly changing.
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32. People are increasingly
enacting their social lives in
computer mediated
channels.
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33. Examples of social media /
big data social insights
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40. Social media are being used
to capture big social data.
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41. Obtaining social media trace data
“Scraping”
APIs
(rare) public datasets
Partnerships
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42. Scraping - using software to
download and store data
from publicly available site.
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43. Application Performance Interfaces
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45. Available datasets
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46. Partnerships
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48. Issues with social media
trace data
Access
Representing results
Representativeness
Validity
Cross-channel difficulty
Appropriate skill sets
Ethics
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49. Access
Data often owned by
private corporations.
Need special skills to
access.
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50. Representing results
Probability testing breaks
down.
Visualization is common,
but limited.
Training audiences for new
data analysis.
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51. Representativeness*
How accurately do
social media users
represent the larger
population?
How do you rigorously
sample from social
media?
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52. Validity
Social media users are
performing (though
don’t know scientists
are observing them)
Different sites have
different purposes.
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53. Cross-channel use
How do you track one
user over multiple
social media sites?
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54. Appropriate researcher skill
sets
Combination of
technical and research
skills are required.
New generation of
“data scientists”
coming now.
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55. Ethics
How can a user opt
out?
More on a panel next
session...
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56. Challenges in “Big Data”
Capture
Curation
Storage
Search
Sharing
Transfer
Analysis
Visualization
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57. “Pros”of big data
It’s relatively cheap
Massive scale covers
some sins
It’s inevitable(?)
In some cases, it
works.
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58. Insensitive Borg says...
Big Data will make
surveys obsolete.
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59. Humble Suggestions
More interdisciplinary work.
Propose and fund work to test these issues.
Don’t pretend it isn’t coming OR is a panacea.
We’re just at the beginning of the journey.
Monday, May 20, 13

60. Social media and surveys
project
Can social media data ever replace and/or supplement
social measurement, especially for official statistics,
based on self-reported answers to questions asked of
a representative sample?
Fred Conrad, Michael Schober, Josh Pasek
Monday, May 20, 13

61. Thanks!
Cliff Lampe
cacl@umich.edu
Twitter: @clifflampe
Slideshare: clifflampe
Monday, May 20, 13