The document reports on a study that examined how the inclusion of different types of photos in automatically generated online persona profiles impacts people's perceptions of confusion and informativeness. The study found that including contextual photos increased perceived informativeness while including multiple similar attribute photos increased confusion. The results suggest that including a headshot photo and contextual photos of the same person provides the optimal persona profile design.

1. “Is More Better?”: Impact of
Multiple Photos
on Perception of Persona Profiles
(+Intro to Automatic Persona Generation)
News & Social Media Analytics Team
Social Computing Group
Qatar Computing Research Institute
Hamad Bin Khalifa University

2. The APG Team
Dr. Jisun An
Scientist
Dr. Haewoon Kwak
Scientist
Prof. Jim Jansen
Leader
Soon-Gyo Jung
Engineer
Dr. Joni Salminen
Post-doctoral researcher
+Dr. Lene
Nielsen
IT University
Copenhagen

3. Outline
1. Motivation
• Introduction to APG
• Roadmap
2. User study

4. What is a persona?
• A ‘persona’ is a fictive person describing an
important user group.
• Simplifies numerical data into an easy format:
another human being
• Personas help communicate numbers in the
organization, so that decisions can be made
keeping the end customer in mind.

5. Which one do you prefer?
vs.
“Personas give faces to data.”
A lot of numbers… Austin, a 35-year-old diving
enthusiast.

6. What is Automatic Persona
Generation (APG)?
A methodology and a system for automatically
creating personas from online analytics data.
Current status:
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 with Al Jazeera English, AJ+ Arabic, AJ+ San
Francisco, Qatar Foundation, and Qatar Airways for actual
use.

7. Why automate persona creation?
Personas are usually created with manual methods, such as interviews
and ethnography. Manual methods are expensive, do not cover many
users, and the personas can become outdated. Therefore, even after
creation, organizations cannot be certain the personas accurately
represent their true user base at a given time.
APG can help:
1. Real behavioral data from online analytics and social media
platforms
2. Faster creation time, from access to ready in a matter of hours
3. Updates each month to reflect changes of user preferences
The mission: Better personas  better decisions  better results.

8. Case example:
Al Jazeera Media Network

9. 1.
First, retrieve data from online
channels (e.g., YouTube Analytics).

10. 2.
Then, generate 5-15 personas
from the data sources.

11. 3.
Finally, show the individual personas.
A: Picture
B: Name, age, gender,
location
C: Text description
D: Topics of interest (most
and least)
E: Descriptive quotes
F: Content the persona is
most interested in
(G: Share of this persona of
the overall audience)

12. Of course, more is happening
in the background…
Configuration
Collection
Generation
A matrix of
content interaction patterns
Automatically generated
personas
Collection/Generation/API
information

13. Of course, more is happening
in the background…
Configuration
Collection
Generation
A matrix of
content interaction patterns
Automatically generated
personas
Collection/Generation/API
information
An, J., Kwak, H., & Jansen, B. J. (2017). Personas for
Content Creators via Decomposed Aggregate Audience
Statistics. In Proceedings of Advances in Social Network
Analysis and Mining (ASONAM 2017). Sydney, Australia.
Read:

14. Information architecture:
How to choose the correct
information elements and
layout for a given user,
context or industry?
Comments:
How to find representative,
contextually relevant,
and non-distracting
comments describing the
persona.
Evaluation: How to ensure
personas are complete,
clear, consistent and
credible? How to measure
usefulness of personas for
individuals and
organizations?
Topics of interest:
How to describe the persona’s
interests across platforms and
contexts?
Image: How to generate
and choose correct
persona profile
pictures?
Temporal analysis:
How to analyze change
and stability of
personas in time?
Attributes: How to infer attributes,
such as psychographics, needs and
wants, political orientation and brand
affinities.
Finding better ways to automatically process and choose useful
information from vast amounts of online data. ”Giving faces to data”
Description: How to
describe the persona in a
fluent and useful way?

15. Information architecture:
How to choose the correct
information elements and
layout for a given user,
context or industry?
Comments:
How to find representative,
contextually relevant,
and non-distracting
comments describing the
persona.
Evaluation: How to ensure
personas are complete,
clear, consistent and
credible? How to measure
usefulness of personas for
individuals and
organizations?
Topics of interest:
How to describe the persona’s
interests across platforms and
contexts?
Image: How to generate
and choose correct
persona profile
pictures?
Temporal analysis:
How to analyze change
and stability of
personas in time?
Attributes: How to infer attributes,
such as psychographics, needs and
wants, political orientation and brand
affinities.
Finding better ways to automatically process and choose useful
information from vast amounts of online data. ”Giving faces to data”
Description: How to
describe the persona in a
fluent and useful way?

16. Research question and
hypotheses
• H1a and b: Adding [a: contextual, b: attribute-similar]
images increases the perceived confusion relative to a
headshot image.
• H2a and b: Adding [a: contextual, b: attribute-similar]
images increases the perceived informativeness relative to a
headshot image.
• H3: Image changes to the persona profile that cause
confusion result in lower informativeness.
• RQ1: Do the images incite associations and cultural
assumptions on top of the written information?

17. • Did a user study to see
how people interacted
with personas
• Found that quotes and
images cause judgment
toward the persona
The new goal: Find out if
toxic comments steer
attention away from other
information (and develop
advanced filtering)
Treatments:

18. Data collection:
(29 subjects)

19. Participants:

20. “You are creating a news video about
[International Affairs / Refugees / Israel-
Palestine]. You want to get some insights
on how to pitch your story. As part of
your investigation, you view the following
persona page, looking for content on the
page to see if it can help you pitch your
story. Be sure and TALK ALOUD, saying
what you are looking at and why. Use the
mouse as you normally would. Click as
you normally would but the links are
disabled, just let the moderator know why
you are clicking on some portion of the
page. Once you are finished, let the
moderator know.”
Operationalization:

21. “[I’m] confused about characteristics of this
person.” (P26, T2)  Confused: general
“quotes are not clear, from who they are.”
(P26, T3)  Confused: quotes
“I’m a little confused, all different women”
(P14, T3)  Confused: photos
If a Participant-Treatment involved cues of
confusion (or informativeness), it was coded as
Confusion = 1 (Informativeness = 1), otherwise 0.
[1] T. Tenbrink, “Cognitive Discourse Analysis: accessing cognitive
representations and processes through language data,” Language and
Cognition, vol. 7, pp. 98–137, 2014.
Cognitive Discourse
Analysis [1]:

22. “[I’m] confused about characteristics of this
person.” (P26, T2)  Confused: general
“quotes are not clear, from who they are.”
(P26, T3)  Confused: quotes
“I’m a little confused, all different women”
(P14, T3)  Confused: photos
If a Participant-Treatment involved cues of
confusion (or informativeness), it was coded as
Confusion = 1 (Informativeness = 1), otherwise 0.
[1] T. Tenbrink, “Cognitive Discourse Analysis: accessing cognitive
representations and processes through language data,” Language and
Cognition, vol. 7, pp. 98–137, 2014.
Cognitive Discourse
Analysis [1]:
Fleiss’ kappa = 0.71
Inter-coder agreement
Confusion = A cognitive state of
the user where user verbally
expresses disorientation.
Informativeness = A cognitive
state of the user in which the user
verbally expresses a high degree
of details of the persona.
Concepts

23. We found a significant difference of confusion between
T1 and T3 (p=0.001). In other words, showing the
multiple attribute-similar photos has a statistically
significant impact on confusion. Thus, H1b is supported,
but H1a is not: adding attribute-similar images increases
the perceived confusion relative to a headshot image but
adding contextual images does not increase confusion.
Findings:
We found a significant difference of informativeness
between T1 and T2 (p=0.001) and T1 and T3 (p=0.048),
indicating that the persona profile with one headshot
image differs from those with contextual images by
informativeness. H2a and H2b are supported: adding
contextual images increases the perceived
informativeness relative to a headshot image as does
adding attribute-similar images. However, there is no
statistically significant difference between T2 and T3.
H1a: Not supported
H1b: Supported
H2a: Supported
H2b: Supported

24. We found a significant difference of confusion between
T1 and T3 (p=0.001). In other words, showing the
multiple attribute-similar photos has a statistically
significant impact on confusion. Thus, H1b is supported,
but H1a is not: adding attribute-similar images increases
the perceived confusion relative to a headshot image but
adding contextual images does not increase confusion.
Findings:
We found a significant difference of informativeness
between T1 and T2 (p=0.001) and T1 and T3 (p=0.048),
indicating that the persona profile with one headshot
image differs from those with contextual images by
informativeness. H2a and H2b are supported: adding
contextual images increases the perceived
informativeness relative to a headshot image as does
adding attribute-similar images. However, there is no
statistically significant difference between T2 and T3.
H1a: Not supported
H1b: Supported
H2a: Supported
H2b: Supported

25. We found that T1 has the highest
number of participants with ‘No
confusion & No informativeness’, T2
has the highest number of
participants with ‘No confusion &
informativeness’, and T3 has the
highest number of participants with
‘Confusion & No informativeness’.
Following these frequencies, T2 can
be interpreted as the optimal design
among the ones tested (i.e., persona
description with a headshot and
contextual photos of the same
person than in the headshot).
Design implications:

26. “I would say her search and her interests are
based on who she is and how she was raised
by previous generations, what they educated
her in of their growing up. This has obviously
peaked her interest in race stories; she is
into black American politics because we are
seeing how politics are going in U.S. and both
of those facets feed into human stories. So,
she is an empathetic culturally aware person
that is aware of her own identity who she is
in the general scheme of things.” (P11,
version A)
People are making up stories.
Pictures have an enforcing effect to
sensemaking: participants mention
more often user features that would
not be detected from text only
(black, young).
Remember, we choose the ethnicity
of the persona. Design power !!!
Qualitative insights:

27. Qualitative insights: “from US, living a good life, can’t
relate to refugees -- people who have
rough life.” (version B: three images
of happiness)
“[the persona’s] most striking
features are: cynical, negative, short
attention span.”
“[the persona is] into refugee issues,
or so she says. The quotes counter
that; she’s not interested based on
them.”
People are judging the
personas based on chosen
pictures and quotes.
Design power !!!

28. Qualitative insights: “from US, living a good life, can’t
relate to refugees -- people who have
rough life.” (version B: three images
of happiness)
“[the persona’s] most striking
features are: cynical, negative, short
attention span.”
“[the persona is] into refugee issues,
or so she says. The quotes counter
that; she’s not interested based on
them.”
People are judging the
personas based on chosen
pictures and quotes.
Design power !!!

29. Qualitative insights: “from US, living a good life, can’t
relate to refugees -- people who have
rough life.” (version B: three images
of happiness)
“[the persona’s] most striking
features are: cynical, negative, short
attention span.”
“[the persona is] into refugee issues,
or so she says. The quotes counter
that; she’s not interested based on
them.”
People are judging the
personas based on chosen
pictures and quotes.
Design power !!!

30.  Background information that helps the user
understand the persona: education, job, where in
the U.S. she lives, etc.
 Peripheral information that helps when
producing content: when she reads, if she watches
videos partly or wholly, her rate of engaging with
the content on social media, etc.
 Information about the data sources, explaining
sources, definitions, and representativeness
Since automatically generated personas do not
currently include this level of information, the
informants, in some cases, are left either lacking
the details on persona attributes, or ‘filling in the
gaps’ based on their own experiences, biases, and
stereotypes that they project on the photos.
Information needs are
instrumental to creating
richer persona profiles.
Design implications:

31.  Background information that helps the user
understand the persona: education, job, where in
the U.S. she lives, etc.
 Peripheral information that helps when
producing content: when she reads, if she watches
videos partly or wholly, her rate of engaging with
the content on social media, etc.
 Information about the data sources, explaining
sources, definitions, and representativeness
Since automatically generated personas do not
currently include this level of information, the
informants, in some cases, are left either lacking
the details on persona attributes, or ‘filling in the
gaps’ based on their own experiences, biases, and
stereotypes that they project on the photos.
Information needs are
instrumental to creating
richer persona profiles.
Design implications:

32.  Persona Crowd Experiments that involve
manipulations to persona profiles and examine the
effects on persona perceptions
 User Study 2.0 that deals with multimodal data
(eye-tracking, mouse-tracking, EEG, emotion
tracking, voice recording).
 Persona Perception Scale, quantifying the
measurement of perceptions of end users of
personas.
If you find these topics interesting, collaborate
with us! Just send me an email at
jsalminen@hbku.edu.qa (Joni Salminen)
Future research:

33.  Persona Crowd Experiments that involve
manipulations to persona profiles and examine the
effects on persona perceptions
 User Study 2.0 that deals with multimodal data
(eye-tracking, mouse-tracking, EEG, emotion
tracking, voice recording).
 Persona Perception Scale, quantifying the
measurement of perceptions of end users of
personas.
If you find these topics interesting, collaborate
with us! Just send me an email at
jsalminen@hbku.edu.qa (Joni Salminen)
Future research:

34. Thanks!
Salminen, J., Nielsen, L., Jung, S.-G., An, J., Kwak, H.,
& Jansen, B. J. (2018). “Is More Better?”: Impact of
Multiple Photos on Perception of Persona Profiles. In
Proceedings of ACM CHI Conference on Human
Factors in Computing Systems (CHI2018). Montréal,
Canada.