In his Brands-Only Summit Pre-Conference presentation, People Pattern's Jason Baldridge explains how profile analytics and user segmentation enables more effective product campaigns. He covers recent results on measuring bias at scale, the effect of network structure on virality, and inferring networks from information cascades.

1. SOCIALMEDIA.ORG/SUMMIT2016ORLANDOJANUARY 25–27, 2016
Social network analysis and
audience segmentation
JASON BALDRIDGE
PEOPLE PATTERN

2. Social Network Analysis and
Audience Segmentation
Jason Baldridge
Co-founder, People Pattern
Associate Professor, UT Austin
@jasonbaldridge

3. Quoting Patterns in Political Coverage
Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Pattterns.”
Measuring bias is subjective and hard.
Personal estimates of bias are influenced by the availability heuristic.
57% of Americans perceive media as biased.
73% of conservatives think bias is liberal.
11% of liberals think bias is liberal.
Similarly: husbands and wives both estimate their
contributions to family activities differently.
[Lee & Waite (2005): http://www.jstor.org/stable/3600272]
Read this!

4. Quoting Patterns in Political Coverage
Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Pattterns.”
Automated tracking of quotations from Obama’s speeches.
Red: quoted in
conservative media.
Blue: quoted in
liberal media.

5. Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Pattterns.”
Dimensionality reduction reveals two main bias dimensions:
(one) independent-mainstream & (two) foreign-liberal-conservative.
Quoting Patterns in Political Coverage

6. Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Pattterns.”
Sentiment across two bias dimensions:
more mainstream & conservative correlates with negative sentiment.
Quoting Patterns in Political Coverage

7. Structural Virality
Goel et al. (2015). “The Structural Virality of Online Diffusion”
Information cascades can propagate via broadcast and viral diffusion.
Most cascades contain both broadcast and viral spreading.
Broadcast Viral

8. Structural Virality
Goel et al. (2015). “The Structural Virality of Online Diffusion”
Twitter cascades characterized by structural virality, increasing down and to the right.

9. Structural Virality
Goel et al. (2015). “The Structural Virality of Online Diffusion”
99% of content
adoptions terminate in
a single generation
The largest image and
video cascades are low on
structural virality.
Broadcast is by far the dominant mode to reach large audiences.
This means pay-to-play when you need to go big reliably.

10. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

11. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

12. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

13. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

14. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

15. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

16. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

17. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

18. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

19. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Contagion model: Information infects nodes, which become active.
Information spreads from active nodes along the network edges.

20. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Given information cascades, infer network using contagion model.

21. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Inferred structure shows emerging and vanishing clusters.
Red: mainstream media. Blue: blogs.
March 2011

22. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Inferred structure shows emerging and vanishing clusters.
Red: mainstream media. Blue: blogs.
June 2011

23. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Inferred structure shows emerging and vanishing clusters.
Red: mainstream media. Blue: blogs.
October 2011

24. Information propagation
Gomez Rodriguez et al (2014). “Uncovering the structure and temporal dynamics of information propagation.”
Blogs and mainstream media swap influence during course of event.
Increased blog influence proportion correlates with social unrest.

25. Is virality/contagion
a bad metaphor?
Taylor Swift has 65 million Twitter followers who can receive her messages.
One individual cannot sneeze on and infect that many people simultaneously.
The likelihood of disease infection increases independently with exposure to
different infected individuals, but “infection” by an idea increases greatly
when exposed to it by multiple, independent parties.

26. Majority illusion
Lerman et al. (2015). “The Majority Illusion in Social Networks.”
The connectedness of “infected” people greatly impacts the perception of others.
A minority opinion can appear extremely popular for each individual (left side).

27. Majority illusion
Lerman et al. (2015). “The Majority Illusion in Social Networks.”
The connectedness of “infected” people greatly impacts the perception of others.
A minority opinion can appear extremely popular for each individual (left side).

28. Majority illusion
Lerman et al. (2015). “The Majority Illusion in Social Networks.”
The connectedness of “infected” people greatly impacts the perception of others.
A minority opinion can appear extremely popular for each individual (left side).

29. Strength of weak ties
Brokerage positions expose their network to diverse information.
Easy to establish weak links in social media, but increases cognitive load.
Embedded position Brokerage position
Kang and Lerman (2015). “User Effort and Network Structure Mediate Access to Information in Networks.”

30. Personality classification
Yarkoni (2010). “Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers.”
Language production provides a window on personality at scale.

31. Ad Targeting and Personality
Chen et al. (2015). “Making Use of Derived Personality: The Case of Social Media Ad Targeting.”
Twitter users whose language indicates higher openness and lower
neuroticism are more likely to respond positively to an ad.

32. Race and sharing
http://www.theatlantic.com/technology/archive/2015/10/race-social-media/408889/
Frequency of sharing for topics on social media varies by race.
Events or entertainment Education or schools

33. Race and sharing
http://www.theatlantic.com/technology/archive/2015/10/race-social-media/408889/
Frequency of sharing for topics on social media varies by race.
Events or entertainment Education or schools
Re “race”: please
read this book.

34. Tailored audiences
People Pattern and Smarty Pants Vitamins case study.
Human analysis and machine learning can be used to characterize
and identify personas using social media profiles.
+

35. Tailored audiences
People Pattern and Smarty Pants Vitamins case study.
Interest prediction and extraction of interest-specific keywords.
Promoted tweet copy informed by persona-based keywords.
+

36. Tailored audiences
People Pattern and Smarty Pants Vitamins case study.
Persona-based campaigns with audience-driven ad copy
produced higher engagement at lower cost per conversion.
+
Conversions
0
60
120
180
240
Control Overscheduled Parent Grab & Go
Cost per conversion
0
10
20
30
40

37. Micro-segmentation
We have limited attention and many options.
The best, most relevant content is often created by those
with very similar passions, interests, and demographics.
Cheryl Baldridge
• JD, Yale
• Lives in the southern USA
• Mother of profoundly gifted
children
• Homeschooler
• Commitment to STEM
• African-American
Doresa Jennings
• PhD, BGSU
• Lives in the southern USA
• Mother of profoundly gifted
children
• Homeschooler
• Commitment to STEM
• African-American
Dr. J creates a lot of original text and video.
My busy wife makes time for it all.
Other content is less compelling for her.
http://kdacademy.blogspot.com/
https://www.youtube.com/user/DAJedu

38. Conclusion
Large scale analysis of networks and documents reveals hidden patterns.

39. Conclusion
Pay-to-play to reliably get your word out.
Large scale analysis of networks and documents reveals hidden patterns.

40. Conclusion
Audience understanding is essential:
demographics, personality and microsegment relevance.
Pay-to-play to reliably get your word out.
Large scale analysis of networks and documents reveals hidden patterns.

41. Jason Baldridge
Co-founder, People Pattern
Associate Professor, UT Austin
Web: http://www.jasonbaldridge.com/
Twitter: @jasonbaldridge
THANKS!
Web: http://www.peoplepattern.com/
Twitter: @peoplepattern

42. Addendum
• This talk incorporates results and images from many different research papers by people working primarily in social network
analysis.
• As such, this talk is a synthesis of that work put together into a narrative to introduce key abilities and results. I felt this high-level
view is exciting and provides great context for work we are doing at People Pattern. Also, I was really impressed by the work
researchers are doing in social network analysis and wanted to share even a glimpse of the problems they are tackling and what
they are finding.
• The high-level progression of this talk is:
• Document analysis at scale: meme tracking combined with other variables like sentiment and bias
• Social network at scale: information cascades and virality, inference of social networks given meme-like information as
contagions.
• The node level perspective and its effects on what an individual sees and shares: Illusions, effort and overload, topics,
personality and demographics.
• Personas and segmentation: grouping based on demographics and interests.
• The last segment on personas and demographics is work done at People Pattern. I stress that neither I nor People Pattern was
involved with the research papers cited in the other slides. My own academic research focuses on natural language processing,
especially machine learning for learning syntactic parsers and performing geolocation using text. For more on those topics, see:
http://www.jasonbaldridge.com/papers
• Much of the material in this talk is condensed from a longer talk I did at IzeaFest. Details on that, including the slides with more
detail references and links to PDF’s of all cited work, are available on this post on my blog: https://bcomposes.wordpress.com/
2015/10/23/references-for-my-izeafest-talk/

43. References
• Chen et al. (2015). “Making Use of Derived Personality: The Case of Social Media Ad Targeting.” -
http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10508
• Cheng et al. (2015). “Antisocial Behavior in Online Discussion Communities.” - http://arxiv.org/abs/
1504.00680
• Friggeri et al. (2015). “Rumor Cascades.” - http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/
paper/view/8122
• Goel et al. (2015). “The Structural Virality of Online Diffusion.” - https://5harad.com/papers/twiral.pdf
• Gomez-Rodriguez et al. (2014). “Quantifying Information Overload in Social Media and its Impact
on Social Contagions.” - http://arxiv.org/abs/1403.6838
• Gomez Rodriguez et al. (2014). "Uncovering the structure and temporal dynamics of information
propagation." - http://www.mpi-sws.org/~manuelgr/pubs/S2050124214000034a.pdf
• Iacobelli et al. (2015). “Large Scale Personality Classification of Bloggers.” - http://
www.research.ed.ac.uk/portal/files/12949424/
Iacobelli_Gill_et_al_2011_Large_scale_personality_classification_of_bloggers.pdf

44. References
• Kang and Lerman (2015). “User Effort and Network Structure Mediate Access to Information in
Networks.” - http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10483
• Kooti et al. (2015). “Evolution of Conversations in the Age of Email Overload.” - http://arxiv.org/abs/
1504.00704
• Kulshrestha et al (2015). “Characterizing Information Diets of Social Media Users.” - https://
www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/viewFile/10595/10505
• Lerman et al. (2015). “The Majority Illusion in Social Networks.” - http://arxiv.org/abs/1506.03022
• Leskovec et al. (2009). “Meme-tracking and the Dynamics of the News Cycle.” - http://
www.memetracker.org/quotes-kdd09.pdf
• Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting
Patterns.” - http://snap.stanford.edu/quotus/
• Weng et al. (2014). “Predicting Successful Memes using Network and Community Structure.” - http://
arxiv.org/abs/1403.6199
• Yarkoni (2010). “Personality in 100,000 Words: A large-scale analysis of personality and word use
among bloggers.” - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2885844/

45. Learn more about past and
upcoming events
SOCIALMEDIA.ORG/EVENTS
SOCIALMEDIA.ORG/SUMMIT2016ORLANDOJANUARY 25–27, 2016