Learn about gaining agile intelligence with open analytics from social media.

1. Building Effective Frameworks
for
Social Media Analysis

2. Agenda
• Social Media: An INT perspective
• Common Analytic Pitfalls
• An Analytic Framework
• Case Study: Brand Management
– Problem Definition
– Source Selection
– Data Capture
– Data Reporting
– Data Analysis
• Ways Forward, Future Analysis
• Questions?

3. Intelligence
• Intelligence is information that has been
transformed to meet an operational need
Operational Lens
Data Intelligence

4. Intelligence Cycle
No matter what method you use…
…analysis is an iterative process

5. Social Media: The INT Perspective
Social Media gets the best
and worst of three disciplines:
HUMINT
– HUMINT
• Pros: Reveals intentions
• Cons: Can be unreliable
– OSINT
• Pros: Fast, Accessible
OSINT SIGINT • Cons: Noise
– SIGINT
• Pros: Network, High Volume
• Cons: Noise

6. Social Media Analysis Goals
• Need to have an end-goal with value to the
organization (operational lens)
• Need to ensure cyclical feedback occurs from
collection, processing, analysis, and
consumption
• Need to make sure that a particular network is
the right source for the task

7. Common Misconceptions
• Social media is not a panacea
– Not everyone uses social media
– Users of social media use it unevenly
– User behavior changes based on situations
• Just because people can talk about anything
does not mean they talk about everything all the
time.

8. Common Pitfalls
• The important thing is often not what people are
saying… but why they are saying it.
• Reporting tools rarely help dig into the why.
• Many common tools, reports, and metrics are
actually misleading:
– Word clouds atomize message context
– Sentiment metrics are often highly inaccurate
– Information in aggregate hides more than it reveals

11. Dangers of Disintegration
Source: Matthew Auer, Policy Studies Journal,
Volume 39, Issue 4, pages 709–736, Nov 2011

12. Analytic Framework
• Data Capture (DC)
• Data Reporting (DR)
• Data Analysis (DA)
– 1. What to measure
– 2. What the data is saying
– 3. What should be done based on the data
Source: Avinash Kaushik, Occam’s Razor Blog
http://www.kaushik.net/avinash/web-analytics-consulting-
framework-smarter-decisions/

13. Analytic Framework
Capture Analysis
Reporting

14. Choosing a Platform
• Social media is still new, evolving; and so
is how we use it.
– Static approaches to social media are flawed
from the outset
– No one metric or set of metrics will always let
you know what is happening
• Need an adaptive platform to facilitate
data capture, reporting, and analysis

15. Case Study: Brand Management
• Industry: Gaming
– Experiencing 10% growth annually
– Overall revenue expected to exceed $80
billion by 2014
• In May, Zenimax Online Studios
announced Elder Scrolls Online
– Elder Scrolls V: Skyrim 2nd largest game of
2011

16. Problem Definition
• As a brand manager, how can I use social
media to track and understand public
attitudes toward my product?
• Challenge is getting relevant information
– Query too large = false positives
– Query too small = miss potential information

17. Source: Twitter
• Twitter has some of the best
analytic potential
– High volume traffic
– High volume user-base
– Open API
• Not without limitations:
– 140 characters
– Limited historical / lookback

18. Platform: Infinit.e
Infinit.e is a
scalable
framework for Visualizing
Analyzing
Retrieving
Enriching
Storing
Collecting
Unstructured documents
&
Structured records

19. Platform: Infinit.e
• Infinit.e supports the extraction of entities
and creation of associations using a
combination of built in enrichment libraries
and 3rd party NLP APIs.

20. Data Capture – Initial Query
• Twitter search for “Elder Scrolls Online”
– Simplest possible way to access information
– RSS feed for 10 days (Jun 27 – July 6 2012)

21. Data Capture - Tagging
{
"_id": "4fea6ddce4b0fa6316c7e07a",
"communityIds": ["4fce07a1e4b06dc8a9107f3b"],
"created": "Jun 26, 2012 10:20:12 PM",
"description": "Twitter search for "Elder Scrolls Online" - started 6/26/2012",
"extractType": "Feed",
"tags": [
"games",
"social",
"entertainment"
],
"title": "Elder Scrolls Online - Twitter“
"url": "http://search.twitter.com/search.rss?q=Elder%20Scrolls%20Online",
"useExtractor": "AlchemyAPI-metadata",
"useTextExtractor": "none“
...
}

22. Data Capture – Entity Map
Hashtag TwitterHandle URL
Who
TwitterHandle
What
Hashtags, Keywords,
URLs
When
Time, Date
Unstructured Keywords Where
Time / Date Stamp Geo (if Available)

23. Data Reporting
• Used Infinit.e’s Flash U/I Widget Framework
– Document Browser (Individual Tweets)
– Entity Significance (Top Entities)
– Sentiment (Top Entities w/ Sentiment)
– Query Metrics (Breakdowns of Query Results)
• Framework allows for additional
visualizations to be constructed as needed
• Export options also available for manual
review (e.g. graphml, excel, pdf)

24. Data Reporting

25. Data Reporting

26. Data Reporting

27. Data Analysis
• Analysis needs to be rooted in the
operational need:
“How can I use social media to track and
understand public attitudes toward my
product”
• Emphasis on hypothesis generation,
testing, and experimentation

28. Data Analysis -> Capture
• Hash tags from an initial subset of Tweets
fed back into the initial query
Initial
Expanded Query
Query
Results
Results
Twitter

29. Data Analysis - Hashtags
• Top hashtags were
almost all generic /
more abstract
– Undermines tracking and
understanding
– Top hashtags tied to
franchise, not to the
game

30. Data Analysis - Sentiment
• Converted URLs into derivative sources
• 35% additional sources
• Larger text sources offer potential value with
sentiment analysis that tweets alone cannot offer

31. Data Analysis - Sentiment
• Top negative and positive scores provided
glimpses into aggregate attitudes
• Provide starting points for additional analysis

32. Data Analysis - Recommendations
• Actionable recommendations allow
decision makers to make changes

33. Future Data Analysis
• Initial conclusions should be starting points
for new analysis
• Broad entity capture allows for:
– Key influencer identification
– Clustering of tweets for segmentation
– Map / Reduce for aggregate functions

34. Infinit.e’s Hadoop Integration

35. Expandable Model
• Identify key influencers on specific topics
• Look at relationships between websites /
blogs and Twitter use (cross-network
analysis)

36. Counting and Summing
• “Traditional” business intelligence analytics
problems solved using aggregate functions:
– Sum
– Count
– Average
– Min
– Max
– Etc.

37. Clustering - Topic
• Topic Extraction
– Key words -> Categories
– Categories -> Related Categories
Keyword Topic Key Value
graphics graphics graphics gameplay.pdf
screenshots graphics story gameplay.pdf
resolution graphics company corporate.txt
quests story … …
zenimax company … …
… …

38. Clustering - Geo

39. Take-Aways
• All data providers can and do change their
formats; users flock to and abandon
platforms – what works today may not
work tomorrow.
• Whatever platform you choose to do
analysis, make sure it’s open and
adaptable or your investment may
degrade over time.

40. Take Aways (Things to Avoid)
• Data puking (less is more)
• Metrics that cannot be tied to actions
• Visualizations / reports that remove
context
• Taking dashboards at face value

41. Take Aways (Things to Do)
• Segment data rather than work in aggregate
• Look for the why behind the message
• Always return to the source material
• Explore alternative explanations
• Always consider the ultimate goal

42. Thank You!
Andrew Strite
www.ikanow.com
astrite@ikanow.com
github.com/ikanow/Infinit.e