=Paper=
{{Paper
|id=Vol-2582/paper1
|storemode=property
|title=Explaining Data Driven Personas to End Users
|pdfUrl=https://ceur-ws.org/Vol-2582/paper1.pdf
|volume=Vol-2582
|authors=Soon-gyo Jung,Joni Salminen,Bernard J. Jansen
|dblpUrl=https://dblp.org/rec/conf/iui/JungSJ20a
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==Explaining Data Driven Personas to End Users==
https://ceur-ws.org/Vol-2582/paper1.pdf
Explaining Data-Driven Personas to End Users
Soon-gyo Jung*, Joni Salminen*§, Bernard J. Jansen*
*Qatar Computing Research Institute, Doha, Qatar
§University of Turku
{sjung, jsalminen, bjansen}@hbku.edu.qa
ABSTRACT layouts [28], not much is known about the digital persona UIs and
their user experience (UX). Apart from exploratory studies [37, 38,
Enabled by digital user data and algorithms, persona user
41], usability problems and interaction patterns in DDP context
interfaces (UI) are moving to digital formats. However, algorithms
remain unchartered. More particularly, there has been little
and user data, if left unexplained to end users, might leave data-
research on how to make the digital persona UIs transparent [31].
driven personas (DDPs) difficult to understand and trust. This is
Transparency refers to providing explanations on how opaque
because the data and the way it is processed are complex and not
algorithms produce information for end users [13].
self-evident, requiring explanations of the DDP information and
This research aims to shed some light into these unexplored
UIs. In this research, we provide a proof of concept for adding
areas, with a specific focus on the design goal of making DDPs
transparency to DDP using a real system UI. Furthermore, we
understandable and trustworthy from the perspective of their
demonstrate ways to add breakdown information that can help
users (e.g., journalists, marketers, online content creators, medical
alleviate user stereotyping associated with the use of personas.
professionals, corporate decision makers, and so on).
As a contribution, we demonstrate ways for adding
KEYWORDS transparency in DDPs by two means: (1) adding explanations of
Personas; digital interfaces; transparency, information design. persona information and how it was produced and (b) adding
breakdown information of the representative persona characteristics,
ACM Reference format:
towards the goal of mitigating stereotypical thinking.
Soon-Gyo Jung, Joni Salminen and Bernard J. Jansen. 2020. Explaining
Therefore, our goals with this research are to demonstrate
Data-Driven Personas to End Users. In Proceedings of the IUI workshop
on Explainable Smart Systems and Algorithmic Transparency in Emerging means to add transparency to DDPs to increase persona users’
Technologies (ExSS-ATEC'20). Cagliari, Italy, 7 pages. understanding and trust towards the personas (both being risks
noted in previous research [8, 23]); and to add information
CSS CONCEPTS breakdowns that show the persona is a composite representation
of a group of users, thereby providing means to alleviate user
• Human-centered computing ~ Human computer interaction
stereotyping, a risk stressed in the persona research [15, 22, 46].
(HCI)
2 Related Literature
1 Introduction Personas were introduced as a HCI technique [29] in software
A persona represents the goals, behaviors and characteristics of a development [9, 17]. There are a variety of benefits attributed to
user segment [4, 29]. While personas are typically created personas [1], such as focusing on user outcomes, consensus
qualitatively from user interviews [14, 17], qualitative approaches building among designers and developers, user-centricity, and
are newly complemented by data-driven personas (DDPs) that use more granular product targeting [33, 36]. Personas provide
quantitative methods, algorithms, and online user data [11, 22, 35]. communication benefits within teams [7] and organizations [28].
This transition from traditional personas to DDPs is associated Personas can enable designers to identify with backgrounds
with the digitalization of persona user interfaces (UIs). While different from their own and realize that the user preferences may
personas are traditionally presented in one or two page paper deviate from their personal preferences [16, 17, 45].
profiles [6, 26], DDPs are presented in a digital UIs that the Yet, to achieve the said benefits, it is critical that personas are
persona users can interact with (see example in Figure 1). perceived as credible and trustworthy by their end users [32]. To
Data-driven persona generation is becoming increasingly achieve trust credibility, one proposed technique is explaining
popular in the industry [27, 34, 35, 43]. The challenge relating to how the personas were created, what design choices were made
this shift is that while research has been done on traditional paper and why. Such transparency has been found especially important
in algorithmic systems that, due to their complexity, may appear
suspicious to end users [10, 13].
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
Figure 1: A DDP from a real production system1. There are several ways for end users to interact with the system, including, e.g.,
selecting the persona, changing the number of personas generated, and filtering the comments of the persona.
Persona transparency is an unexplored area in the HCI The advice from the previous research is that persona creators
literature. Among the rare studies focused on persona should seek to experiment with novel designs of transparency for
transparency, Salminen et al. [42] analyze the impact of DDPs [42]. To this end, we present some explanations and data
explanations added in the persona UI on user perceptions. They breakdowns we have implemented in a persona system. Note that
find that higher transparency (in the form of explanations) these findings represent only the added explanations and
increased the perceived completeness and clarity of the shown breakdowns and do not include an empirical user study on their
personas. However, there was an undesirable effect of the implementation on the persona UX. Such a study is a planned next
explanations decreasing the credibility of the persona. The step in the research agenda.
researchers interpreted this as an indication of a transparency
trade-off, according to which the technical explanations disrupt
the façade of personas being perceived as real people.
It is also worth to note that Salminen et al. [42] implemented
the explanations “forcefully”, meaning that they were shown as
open pop-up type of boxes to the users (see Figure 2). In contrast,
we implement the explanations as interactive tooltip definitions
that the users can reveal by hovering over the tooltip icon.
Overall, computational techniques are becoming more
common in persona development, with several researchers
presenting their versions of algorithmically generated personas
[2, 3, 24, 47]. The users are given access to the generated personas
through system UIs where they can interact with the personas,
including selecting each persona and viewing their information
The users of these DDPs may question the information in persona
profiles because they are unsure of how it was produced. This is a
special concern for DDPs because their creation relies on opaque Figure 2: Explanations implemented in Salminen et al. [42].
algorithmic processes that are often difficult to communicate in These were shown “forcefully” without giving the users ability
layman terms [5]. This difficulty can be seen from the findings of to enable or disable the explanations.
user studies that report issues of confusion and information
design of algorithmically generated DDPs [36–38, 41].
1
https://persona.qcri.org
Table 1: Explanations implemented in the DDP system. Adapted from Salminen et al. [42].
Section Explanation provided Implemented
(✗ = No, ✓ = Yes)
Name Persona’s name is chosen by retrieving common names from a popular online social network of ✗
people with a given age, gender, and country. Tools we use: Python, Pandas, Database
Picture Persona’s picture is chosen from pictures downloaded from online photobanks, tagged for age, ✗
gender, country, and ethnicity. Tools we use: Python, Online photobanks, Database
Demographics Persona’s demographic information (age, gender, country) is retrieved from aggregated YouTube ✗
viewer statistics of this channel’s videos. Tools we use: Python, YouTube API
Job Job is shown based on Facebook audience sizes. The system collects Facebook audience sizes ✓
based on persona’s demographic, interests, and language.
Education Education Level is shown based on Facebook audience sizes. The system collects Facebook ✓
Level audience sizes based on persona’s demographic, interests, and language.
Relationship Relationship Status is shown based on Facebook audience sizes. The system collects Facebook ✓
Status audience sizes based on persona's demographic, interests, and language.
Topics of Topics of interest are retrieved by classifying the content to descriptive categories and choosing ✓
Interest the most corresponding ones for this persona. Tools we use: Python, Pandas, Scikit-learn (Latent
Dirichlet Allocation), supervised machine learning, Database
Most Viewed Most viewed contents are retrieved from the aggregated view counts of YouTube videos and are ✓
Contents chosen to describe the taste of this persona. Tools we use: Python, Database, YouTube API
Viewed Persona’s quotes are retrieved from the comments of most viewed videos of this persona. Tools ✓
conversations we use: Python, Database, YouTube API
Audience Size Audience size is calculated by searching the number of people on Facebook with similar attributes ✓
to this persona, including age, gender, country, language, and topics of interest. Tools we use:
Python, Facebook Marketing API, Database
multiple online analytics and social media platforms, including
Facebook Insights, YouTube Analytics, and Google Analytics.
3 Implementing DDP Transparency Thus, we implement the explanations of the previous user
We adopted a simple design principle for transparency: explain to study [42] in APG. The following sections demonstrate the
the user what the information is and where it comes from. The implementation through practical examples from the UI. We first
explanations were then crafter by one of the researchers for all demonstrate the explanations and then the data breakdowns. Note
the information elements in the persona UI, as defined in Table 1. that all explanations require the user to hover either the tooltip
After this, the other researchers gave feedback on the wording and icon (the small question mark in Figure 4) or the element itself to
content of the explanations. Finally, after being reviewed by show. The breakdowns require the user to click on the breakdown
everyone in the research team, the explanations were icon (the small magnifying glass in Figure 3).
implemented in the persona system.
Note that the explanations are the same as the ones used in a 3.2 Explanations
previous user study [42]. That study, however, tested only Figure 3 demonstrates the explanation for the stability indicator.
persona mockups, not a live system. Here, we implement the The stability function informs the user of how frequently this
explanations in a live system for real client organizations1. persona appears in different persona sets over time. If the persona
appears often, he or she is labeled as a “Loyal” persona. Otherwise,
3.1 Persona System the persona is labeled as a “Occasional” persona.
The persona system is called Automatic Persona Generation Figure 4 shows the sentiment explanation. Sentiment score is
(APG) and it has been widely reported in previous research [2, 3, calculated as an aggregate score from the comments associated
18, 19, 39]. APG is both a system and methodology for generating with the persona and describes the persona’s overall attitude.
personas from online analytics and social media user data. The Figure 5 shows the explanation for topics of interest. Topics
system uses application programming interfaces (APIs) to are reflective of the content consumption preferences of online
automatically collect online user data with channel owners’ audience personas [39]. Similarly, most viewed contents describe
permission. It then carries out algorithmic data analyses and the content that the group corresponding to the persona has most
outputs a set of DDPs that the end users can interact with using viewed (see Figure 6). The comments shown in the persona profile
the system UI. APG uses a robust Web framework for Python are inferred from this content (see Figure 7). Each persona has
(Flask) and a stable back-end database (PostgreSQL). It supports
Figure 4: Explanation for the sentiment of the persona.
Figure 3: Explanation for the stability of the persona.
Figure 5: Explanation for topics of interest.
Figure 6: Explanation for the viewed contents.
Figure 8: Explanation for the audience size.
Figure 7: Explanation for the quotes.
demographic traits (age, gender, location) and topics of interest. this persona. In other words, there is diversity within the persona.
Based on these, audience size is calculated. This corresponds to In a similar vein, Figure 10 illustrates the distribution of topics of
the number of people with the said characteristics (see Figure 8). interest. It shows how much, in quantitative terms, the persona
prefers, or does not prefer, a given topic. Again, as these measures
3.3 Data Breakdowns are calculated using machine learning models (see [3]), we can
To reduce stereotyping and to facilitate the understanding of the obtain and present a probabilistic score for the persona users.
data, APG provides breakdowns of information. Figure 9 shows
the demographic groups that have the highest quantitative 3.4 Explaining Algorithmic Process of DDPs
association with the content engagement pattern that the persona One challenging – perhaps even the most challenging – aspect of
is based on. The point is to show to the users that even though the explanation in DDPs is the functioning of the core algorithm. This
persona has a representative demographic group (in this case, has previously been done using equations [2, 3, 39, 40] and figures
Male 25-34 India), there are also other demographic group that fit, (see Figure 12).
with different degrees of association, to the behavioral pattern of
Explaining Data-Driven Personas S.-G. Jung et al.
(see Figure 11) and complex mathematical denotations (see Figure
12) explanations. Essentially, communicating algorithmic
processes is a hard problem to solve as the process has many steps
and a high degree of technical complexity. Explaining these
processes in a simple graph, text, or table seem not very user-
friendly. As a future course of action, we are planning to produce
an explainer video – the advantages of a video are many: we can
use different screens, views, and animations to simplify the
algorithm; we can break down the process into a logical narrative;
we can give examples that make the storyline more concrete; and
we can support the conveying of the message with visual, textual
and auditory information (i.e., voiceover). This facilitates learning
and understanding by different user types.
Figure 9: Breakdown of demographic groups, intended to
decrease stereotyping by showing that not only one
demographic group corresponds to the shown persona. For
example, figure shows that although the dominant
demographic group is male, also females (e.g., Female 18-24 Figure 11: Stepwise explanation of the APG algorithm [3].
India) correspond to the behavioral pattern of the persona.
Figure 12: Symbolic explanation of the APG algorithm [2].
4 Discussion
4.1 Contribution
Personas are said to be cognitively compelling [4] and empathetic
[21, 25], as they put a human face on otherwise obscure user data.
Pruitt and Grudin [30] outline that psychological theory explains
why personas should be engaging, pointing out that personas
provide a conduit for conveying a broad range of user attributes.
Figure 10: Distribution of the persona’s topical interests.
Yet, personas have been repeatedly challenged in the literature
To generate the personas, APG uses the underlying data to for their “imaginary” nature [8], abstraction and lack of credibility
obtain a grouped interaction matrix V (𝑉 = 𝑔 ∗ 𝑐), where the [23, 32]. DDPs provide features and functionalities that can
columns of the said matrix, for our task, are video content (c) and provide partial or complete solutions to long-standing persona
the rows represents demographic user groups (g). The element in weaknesses, such as being slow to create and rapidly expiring [2]
the matrix are the view-counts of the videos for each demographic and being subjective instead of fact-based [3, 8]. In addition,
group. The system then applied non-negative matrix factorization personas have been criticized for lack a real value to enhance user
(NMF) [20] to V to discern p latent video viewing behaviors, using insights, especially in the modern environment with many other
the resultant weights from the NMF. These groups, p, are then analytics tools are available for probing into online audiences [1].
enriched by adding attributes, including a name, profile picture, Using digital persona UIs could potentially provide ailments to
their topic of interest among others. enhance the value decision makers get from personas.
Various ways to explain this algorithmic approach have been
attempted in previous literature, including a simple stepwise list
ExSS-ATEC'20, March 2020, Cagliari, Italy S.-G. Jung et al.
Nonetheless, due to prevalence of paper as the default choice workshops. For persona adoption in the target organization,
of UI for personas, there is currently a lack of empirical studies explanations can be useful but not necessarily enough.
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