CITE: "Salminen, J., Sercan, Ş., Haewoon, K., Jansen, B. J., An, J., Jung, S., Vieweg, S., Harrell, F. (2017). Generating Cultural Personas from Social Data: A Perspective of Middle Eastern Users. In Proceedings of The Fourth International Symposium on Social Networks Analysis, Management and Security (SNAMS-2017). Prague, Czech Republic, 21–23, August." Download paper: http://jonisalminen.com/wp-content/uploads/2018/08/Generating-Cultural-Personas-From-Social-Data_SNAMS2017.pdf *** Automatic Persona Generation (APG) is a system and methodology developed at Qatar Computing Research Institute, Hamad Bin Khalifa University. The goal is to give faces to social and online analytics data. Personas can be generated from YouTube, Facebook, and Google Analytics data. The system can be found at https://persona.qcri.org

1. Generating Cultural Personas
From Social Data
A Perspective of Middle Eastern Users
Joni Salminen, Sercan Şengün, Haewoon Kwak, Bernard
J. Jansen, Jisun An, Soon-Gyo Jung, Sarah Vieweg, D.
Fox Harrell
SNAMS17, Prague

2. The team at QCRI
Prof. Jim Jansen Dr. Jisun An Dr. Haewoon Kwak
Dr. Joni SalminenMSc. Soon-Gyo Jung

3. What is a persona?
• ‘Persona’ is a fictive person describing an
underlying user group.
• Simplifies numerical data into easy-to-understand
representation: another human being
• Helps communicate numbers in the organization,
so that content, marketing, or product decisions
can be made while keeping the end user in mind.

4. Which one do you prefer?
vs.
“Personas give faces to data.”

5. Automatic Persona Generation
Methodology for automatically creating personas
from online social media data.
Currently:
a. processing hundreds of millions of user interactions from
YouTube and Facebook.
b. stable and robust system using Flask framework and
PostgreSQL database
c. deployed with Al Jazeera English, AJ+ Arabic, Qatar
Foundation, and AJ+ San Francisco for beta testing.

6. Persona generation:
Process
1) Collecting data via social media APIs
• Content information (e.g., title of content)
• Behavior information (e.g., number of views per content)
2) Identifying distinct behavioral patterns of the user in regards to
content
3) Identifying impactful demographic groups from the set of
distinct behavioral patterns
4) Creating skeletal personas via demographic attributes from the
data set
5) Enriching the skeletal personas with more information.

7. Persona generation:
Matrix decomposition

8. An example: Bakkar
Name
Picture
Demographic
information
Topics of
interest
Most viewed
videos (YouTube
channel)

9. Advantages and
disadvantages
Automatic persona
generation
Traditional persona generation
+ Fast (~2 days), accurate
(based on latent behavioral
patterns), updates in time
Depth of information
- Broadness of information Slow (takes months), inaccurate
(interviewing or observation bias),
personas become stale

10. Advantages and
disadvantages
Automatic persona
generation
Traditional persona generation
+ Fast (~2 days), accurate
(based on latent behavioral
patterns), updates in time
Depth of information
- Broadness of information Slow (takes months), inaccurate
(interviewing or observation bias),
personas become stale
We’re interested in enriching
automated personas with qualitative
insights.

11. Combining quantitative and
qualitative data to generated
cultural personas
• Data:
• Quantitative: 12 M views on 2,443 videos on AJ+ YouTube channel
(November, 2015 – April, 2017)
• Qualitative: 5 interviews (45–90 min)
• Analysis:
• Quantitative: APG
• Qualitative: Action-Implicative Discourse Analysis (coding)
• Mapping: Manually based on demographic and topical interests.

12. Generating cultural persona representations

13. Future work
• Extending the cultural inquiry
• Improving the core system (algorithm, usability, etc.)
• Looking for co-authors!

14. Information architecture:
Choosing the correct information
elements & layout per user or
industry.
Comments: Finding
representative, relevant and
non-toxic comments describing
the persona.
Evaluation: Validating accuracy,
consistency, and usefulness of
personas for individuals and
organizations.
Topics of interest: Creating topic
classifications of online content
and discovering probable
interests by bridging social
media platforms.
Plenty of research streams!
Description: Describing the person
in a fluent way with attributes
relevant to decision makers.
Discovering better ways to computationally process
and choose useful representations from vast
amounts of online data (”giving faces to data”).
Image: Using generative
adversarial network to generate
persona profile pictures.
Story selection: predicting and
choosing content for personas
or content creators.
Temporal analysis: Observing
change in personas over time.
Cross-platform data mapping:
Creating rounded personas.

15. The team at QCRI
Prof. Jim Jansen Dr. Jisun An Dr. Haewoon Kwak
Dr. Joni SalminenMSc. Soon-Gyo Jung
jsalminen@hbku.edu.qa