A presentation at ICSEC17. Doha, Qatar. Read more: https://persona.qcri.org *** 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. Big Data, Small Personas
Research Agenda for Automatic Persona Generation
Joni Salminen, Soon-gyo Jung, Bernard J.
Jansen, Jisun An, Haewoon Kwak
Qatar Computing Research Institute
Hamad Bin Khalifa University

2. What is a persona?
• ‘Persona’ is a fictive person (picture, name, age…)
describing a core user group.
• Simplifies numerical data into easy-to-understand
representation: another human being
• Helps communicate user information in the
organization, so that content creation, marketing, and
product development can be done keeping the users
in mind at all times.

3. Automatic Persona Generation
Methodology and system for automatically
creating personas from online social media data.
Currently:
a. processing hundreds of millions of user interactions from
YouTube, Facebook, and Google Analytics.
b. stable and robust system using Flask framework, PostgreSQL
database, and Pandas/scikit-learn data analysis library.
c. deployed in Al Jazeera English, AJ+ Arabic, Qatar Foundation,
AJ+ San Francisco, and Qatar Airways.

4. Why automate personas?
Personas are usually created with manual methods (e.g., interviews).
The methods are expensive, slow, and the personas quickly become
outdated. Therefore, even after creation, organizations cannot be
certain the personas accurately represent their true customers.
Our solution:
1. Real data  better personas
2. Faster creation  better personas
3. Updates each month  better personas
Better personas  better decisions  better results.

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

6. Case example
Al Jazeera

7. 1.First, retrieve data from YouTube Analytics.

8. 2.Second, generate personas and show them to
end users.

9. 3.Third, show individual personas.

10. …of course, a lot more is happening
in the background.

11. Information
architecture: Choosing
the correct information
elements and layout for
a given 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:
Classifying topics of online
content and discovering
probable interests across
social media platforms.
APG: Platform for research
Description: Generating
fluent text descriptions of
the personas.
Discovering better ways to computationally process
and choose useful representations from vast
amounts of online data (”giving faces to data”).
Image: Using neural
networks to generate
persona profile pictures.
Story selection: predicting
and choosing content for
personas or content
creators.
Temporal analysis:
Observing change in
personas over time.

12. Determining the right
information content and layout
Research Objective: There is an extremely high number of
potential information elements (e.g., demographics,
psychographics, interests, political affiliation…) and types
(image, text, sizes, colors…) that can be chosen and shown
for a given user. However, the screen real-estate is limited,
and obviously some information is not relevant in all use
cases, as user preferences and contexts vary a great deal.
Therefore, how to ensure a user receives the right
information at the right time?

13. Determining the right
information content and layout
Plan: Conduct theory-driven A/B and/or multivariate
experiments with real users of APG (e.g., journalists, digital
marketers). Measure reaction with Web analytics metrics
(time-on-site, number of clicks) and mouse tracking (AOIs,
heatmaps), and analyze it according to known information
on the users.

14. Persona Evaluation Scale
Research Objective: Personas are notoriously difficult to
validate and evaluate. As fictive user representations based on
real data, they can be seen subject to interpretations from
end-users of personas. These interpretations can be multi-
dimensional and include elements, such as empathy,
perceived accuracy and liking. We develop an evaluation
scale and validate it among end users of personas.

15. Persona Evaluation Scale
Plan: Develop the scale, validate with pilot study, and
then conduct a large scale survey for persona users.

16. Thank you!