An J.,

Jansen B.J.,

Jung S.G.,

Kwak H.,

Salminen J.,

English

Institutional Knowledge at Singapore Management University

Singapore Management University Institutional Knowledge at Singapore Management University Research Collection School Of Information Systems School of Information Systems 6-2018 Automatically conceptualizing social media analytics data via personas Jung S.G. Salminen J. An J. Kwak H. Jansen B.J. Follow this and additional works at: https://ink.library.smu.edu.sg/sis_research  Part of the Databases and Information Systems Commons, and the Social Media Commons Citation Jung S.G., Salminen J., An J., Kwak H., and Jansen B.J.. Automatically conceptualizing social media analytics data via personas. (2018). Proceedings of the 12th International AAAI Conference on Web and Social Media, ICWSM 2018, Palo Alto, California USA, June 25-28. 715-716. Research Collection School Of Information Systems. Available at: https://ink.library.smu.edu.sg/sis_research/5342 This Conference Proceeding Article is brought to you for free and open access by the School of Information Systems at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School Of Information Systems by an authorized administrator of Institutional Knowledge at Singapore Management University. For more information, please email cherylds@smu.edu.sg.   Automatically Conceptualizing Soon-Gyo Jung, Joni Salminen, Jisun An, Haewoon Kwak, Bernard J. Jansen Qatar Computing Research Institute, HBKU {sjung, jsalminen, jan, hkwak, bjansen}@hbku.edu.qa    Abstract Social media analytics is insightful but can also be difficult to use within organizations. To address this, we present Au-tomatic Persona Generation (APG), a system and methodol-ogy for quantitatively generating personas using large amounts of online social media data. The APG system is op-erational, deployed in a pilot version with several organiza-tions in multiple industry verticals. APG uses a robust web and stable back-end database framework to process tens of millions of user interactions with thousands of online digital products on multiple social media platforms, including Face-book and YouTube. APG identifies both distinct and impact-ful audience segments for an organization to create persona profiles by enhancing the social media analytics data with pertinent features, such as names, photos, interests, etc. We demonstrate the architecture development, and main system features. APG provides value for organizations distributing content via online platforms and is unique in its approach to leveraging social media data for audience understanding. APG is online at https://persona.qcri.org.  Introduction   Social media analytics is used for audience analysis. How-ever, this data has disadvantages in certain contexts, being voluminous and granular.  One method of simplifying the presentation of this data is via the use of personas. Personas are employed in domains from software development to sys-tem design to marketing in order to describe and communi-cate about core users, customers, or audiences. One can think of personas as mental shortcuts that present the who, what, and why of a set of individuals. Personas can assist in defining organizational strategy, develop new products, and improve customer operations. Personas are integrated into many design, product, marketing, content, and advertising development processes.  The Automatic Personas Generation (APG) system lever-ages privacy-preserving aggregated data of audience inter-actions with digital content posted on major online social media and other analytics platforms. APG collects, pro-cesses, and decomposes this actual user data and then                                                  Copyright © 2018, Association for the Advancement of Artificial Intelli-gence (www.aaai.org). All rights reserved. enriches the results with descriptive attributes to produce data-driven persona profiles. APG can produce personas from millions of user interactions within a matter of days, which is far shorter time than traditional persona creation.   Here, we present an overview of APG, including archi-tecture and development. We then highlight the core fea-tures of APG, with other features left for the demo. We dis-cuss use cases, uniqueness, and commercial efforts. System Overview APG uses a stable, robust, and scalable structure employing (a) the Flask web framework to support the front-end appli-cations, services, and application programming interfaces (APIs), (b) PostgreSQL database for back-end data storage, processing, and (c) Python libraries including Pandas and scikit-learn for data analysis (Jung, et al, 2017).  APG accesses the specific online social media platforms, (e.g., Facebook, YouTube) via the analytics API of the ac-count holders. Typical user profile data from these platforms are demographic variables of gender, age, country location, and which site the user comes from, although at an aggre-gated level. Via the API, the APG system collects the de-tailed interactions of users with the online content. This is accessible only to managers of a particular social media ac-count (e.g., YouTube channel).   To automatically generate personas from this data, our methodology (Jung, et al, 2017) applies a sequential ap-proach, consisting of:  identifying distinct user interaction patterns,  linking these patterns to demographic groups,  identifying impactful demographic groups from the data,  creating base personas via demographic attributes,  enriching these shell personas to create a complete persona.  Social Media Analytics Data via PersonasProceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018)715 Figure 1: APG flowchart from server configuration to data collec-tion, persona generation, and users accessing system. With the complete data flow shown in Fig. 1, we now pre-sent some key features of APG to demonstrate system capa-bilities, with others shown during the live demonstration. System Features As shown in Fig. 2, a user can select the number of personas to generate, from a minimum of 5 and a maximum of 15. The user can also select the desired data source and is given options for viewing the personas. The listing of the personas, combined with the mouseovers, affords the user an over-view of the audience segments in terms of demographic in-formation, as shown in Fig. 2. By hovering over a persona image, the basic attributes are displayed, in this case, ‘Neelix, United States, 3, Male’. The end user can also change content collections or the number of personas, or ap-ply different filterings, such as data set, topics of interest, ethnicity, country, age, or gender. A click on one of the im-ages in the persona listing will display the corresponding persona profile.  There are a multitude of other features and capabilities that will be presented during the live demonstration, includ-ing comparing personas between periods, comparing current user segment size for personas against potential audience size, and displaying personas related to each item of content (Salminen, et al, 2018). Results and Discussion Use Cases for APG APG is currently deployed, in pilot version, in multiple  organizations and is used to understand audiences on multi-ple social platforms, design engaging digital content, and communicate more effectively concerning audiences, users, and customers. Comparison to Other Systems To the best of our knowledge, APG is the first deployed sys-tem to use large amounts of online behavioral user data for quantitative and automatic persona generation.  Commercialization Efforts Presently, APG is in beta use with three major multi-na-tional corporations, one major non-profit organization, and two SMEs in the retail vertical, with efforts underway for full deployment. The APG project is the recipient of devel-opment funding to take the system to Technology Readiness Level 8 (TRL-8). APG has an evaluation license in place and is participating in an Entrepreneur in Residence (EIR) program, with start-up funding in progress.   Conclusion In this demonstration, we will show the APG capability for automatically creating personas from large scale, quantita-tive, aggregated user data from major social media plat-forms. The personas are generated from real data reflecting real user behavior and are updated automatically. APG can be applied to a wide range of contexts and can be used in a stand-alone mode or used in conjunction with or to supple-ment personas created via qualitative means. References Jung, S., An, J., Kwak, H., Ahmad, M., Nielsen, L., & Jansen, B. J. (2017, 6-11 May). Persona Generation from Aggregated Social Media Data. Paper presented at the ACM Conference on Human Factors in Computing Systems 2017 (CHI2017), Denver, CO.  Salminen, J., Nielsen, L., An, J., Jung, S.G., Kwak, H., and Jansen, B. J. (2018) Is More Better?”: Impact of Multiple Photos on Per-ception of Persona Profiles. ACM CHI Conference on Human Fac-tors in Computing Systems (CHI2018), Montréal, Canada, 21-26 April.  Figure 2: APG for selecting the content source platform (e.g., Fa-cebook, YouTube), the number of personas to generate (5 to 15), and the view type (various formats of viewing the relationship be-tween content and personas). Th options in the screen capture are set to YouTube and 6 personas. 716

Automatically conceptualizing social media analytics data via personas

Singapore Management University 
Institutional Knowledge at Singapore Management University 
Research Collection School Of Information 
Systems School of Information Systems 
6-2018 
Automatically conceptualizing social media analytics data via 
personas 
Jung S.G. 
Salminen J. 
An J. 
Kwak H. 
Jansen B.J. 
Follow this and additional works at: https://ink.library.smu.edu.sg/sis_research 
 Part of the Databases and Information Systems Commons, and the Social Media Commons 
Citation 
Jung S.G., Salminen J., An J., Kwak H., and Jansen B.J.. Automatically conceptualizing social media 
analytics data via personas. (2018). Proceedings of the 12th International AAAI Conference on Web and 
Social Media, ICWSM 2018, Palo Alto, California USA, June 25-28. 715-716. Research Collection School Of 
Information Systems. 
Available at: https://ink.library.smu.edu.sg/sis_research/5342 
This Conference Proceeding Article is brought to you for free and open access by the School of Information 
Systems at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in 
Research Collection School Of Information Systems by an authorized administrator of Institutional Knowledge at 
Singapore Management University. For more information, please email cherylds@smu.edu.sg. 
 
 
Automatically Conceptualizing 
Soon-Gyo Jung, Joni Salminen, Jisun An, Haewoon Kwak, Bernard J. Jansen 
Qatar Computing Research Institute, HBKU 
{sjung, jsalminen, jan, hkwak, bjansen}@hbku.edu.qa  
 
 
Abstract 
Social media analytics is insightful but can also be difficult 
to use within organizations. To address this, we present Au-
tomatic Persona Generation (APG), a system and methodol-
ogy for quantitatively generating personas using large 
amounts of online social media data. The APG system is op-
erational, deployed in a pilot version with several organiza-
tions in multiple industry verticals. APG uses a robust web 
and stable back-end database framework to process tens of 
millions of user interactions with thousands of online digital 
products on multiple social media platforms, including Face-
book and YouTube. APG identifies both distinct and impact-
ful audience segments for an organization to create persona 
profiles by enhancing the social media analytics data with 
pertinent features, such as names, photos, interests, etc. We 
demonstrate the architecture development, and main system 
features. APG provides value for organizations distributing 
content via online platforms and is unique in its approach to 
leveraging social media data for audience understanding. 
APG is online at https://persona.qcri.org.  
Introduction   
Social media analytics is used for audience analysis. How-
ever, this data has disadvantages in certain contexts, being 
voluminous and granular.  One method of simplifying the 
presentation of this data is via the use of personas. Personas 
are employed in domains from software development to sys-
tem design to marketing in order to describe and communi-
cate about core users, customers, or audiences. One can 
think of personas as mental shortcuts that present the who, 
what, and why of a set of individuals. Personas can assist in 
defining organizational strategy, develop new products, and 
improve customer operations. Personas are integrated into 
many design, product, marketing, content, and advertising 
development processes. 
 The Automatic Personas Generation (APG) system lever-
ages privacy-preserving aggregated data of audience inter-
actions with digital content posted on major online social 
media and other analytics platforms. APG collects, pro-
cesses, and decomposes this actual user data and then 
                                                 
Copyright © 2018, Association for the Advancement of Artificial Intelli-
gence (www.aaai.org). All rights reserved. 
enriches the results with descriptive attributes to produce 
data-driven persona profiles. APG can produce personas 
from millions of user interactions within a matter of days, 
which is far shorter time than traditional persona creation.  
 Here, we present an overview of APG, including archi-
tecture and development. We then highlight the core fea-
tures of APG, with other features left for the demo. We dis-
cuss use cases, uniqueness, and commercial efforts. 
System Overview 
APG uses a stable, robust, and scalable structure employing 
(a) the Flask web framework to support the front-end appli-
cations, services, and application programming interfaces 
(APIs), (b) PostgreSQL database for back-end data storage, 
processing, and (c) Python libraries including Pandas and 
scikit-learn for data analysis (Jung, et al, 2017). 
 APG accesses the specific online social media platforms, 
(e.g., Facebook, YouTube) via the analytics API of the ac-
count holders. Typical user profile data from these platforms 
are demographic variables of gender, age, country location, 
and which site the user comes from, although at an aggre-
gated level. Via the API, the APG system collects the de-
tailed interactions of users with the online content. This is 
accessible only to managers of a particular social media ac-
count (e.g., YouTube channel).  
 To automatically generate personas from this data, our 
methodology (Jung, et al, 2017) applies a sequential ap-
proach, consisting of: 
 identifying distinct user interaction patterns, 
 linking these patterns to demographic groups, 
 identifying impactful demographic groups from 
the data, 
 creating base personas via demographic attributes, 
 enriching these shell personas to create a complete 
persona. 
 
Social Media Analytics Data via Personas
Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018)
715
 
Figure 1: APG flowchart from server configuration to data collec-
tion, persona generation, and users accessing system. 
With the complete data flow shown in Fig. 1, we now pre-
sent some key features of APG to demonstrate system capa-
bilities, with others shown during the live demonstration. 
System Features 
As shown in Fig. 2, a user can select the number of personas 
to generate, from a minimum of 5 and a maximum of 15. 
The user can also select the desired data source and is given 
options for viewing the personas. The listing of the personas, 
combined with the mouseovers, affords the user an over-
view of the audience segments in terms of demographic in-
formation, as shown in Fig. 2. By hovering over a persona 
image, the basic attributes are displayed, in this case, 
‘Neelix, United States, 3, Male’. The end user can also 
change content collections or the number of personas, or ap-
ply different filterings, such as data set, topics of interest, 
ethnicity, country, age, or gender. A click on one of the im-
ages in the persona listing will display the corresponding 
persona profile. 
 There are a multitude of other features and capabilities 
that will be presented during the live demonstration, includ-
ing comparing personas between periods, comparing current 
user segment size for personas against potential audience 
size, and displaying personas related to each item of content 
(Salminen, et al, 2018). 
Results and Discussion 
Use Cases for APG 
APG is currently deployed, in pilot version, in multiple  
organizations and is used to understand audiences on multi-
ple social platforms, design engaging digital content, and 
communicate more effectively concerning audiences, users, 
and customers. 
Comparison to Other Systems 
To the best of our knowledge, APG is the first deployed sys-
tem to use large amounts of online behavioral user data for 
quantitative and automatic persona generation.  
Commercialization Efforts 
Presently, APG is in beta use with three major multi-na-
tional corporations, one major non-profit organization, and 
two SMEs in the retail vertical, with efforts underway for 
full deployment. The APG project is the recipient of devel-
opment funding to take the system to Technology Readiness 
Level 8 (TRL-8). APG has an evaluation license in place 
and is participating in an Entrepreneur in Residence (EIR) 
program, with start-up funding in progress.   
Conclusion 
In this demonstration, we will show the APG capability for 
automatically creating personas from large scale, quantita-
tive, aggregated user data from major social media plat-
forms. The personas are generated from real data reflecting 
real user behavior and are updated automatically. APG can 
be applied to a wide range of contexts and can be used in a 
stand-alone mode or used in conjunction with or to supple-
ment personas created via qualitative means. 
References 
Jung, S., An, J., Kwak, H., Ahmad, M., Nielsen, L., & Jansen, B. 
J. (2017, 6-11 May). Persona Generation from Aggregated Social 
Media Data. Paper presented at the ACM Conference on Human 
Factors in Computing Systems 2017 (CHI2017), Denver, CO.  
Salminen, J., Nielsen, L., An, J., Jung, S.G., Kwak, H., and Jansen, 
B. J. (2018) Is More Better?”: Impact of Multiple Photos on Per-
ception of Persona Profiles. ACM CHI Conference on Human Fac-
tors in Computing Systems (CHI2018), Montréal, Canada, 21-26 
April.  
Figure 2: APG for selecting the content source platform (e.g., Fa-
cebook, YouTube), the number of personas to generate (5 to 15), 
and the view type (various formats of viewing the relationship be-
tween content and personas). Th options in the screen capture are 
set to YouTube and 6 personas. 
716


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Automatically conceptualizing social media analytics data via personas

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