Automatic Persona Generation for Online Content Creators: Conceptual Rationale and a Research Agenda

Salminen, Joni; Jansen, Bernard J.; An, Jisun; Kwak, Haewoon; Jung, Sin‐Ho

doi:10.1007/978-1-4471-7427-1_8

Cited by 21 publications

(7 citation statements)

References 41 publications

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“…However, this was not applicable for our experiments because of the focus shift and the additional effort that this would require by the participants, as well as the complexity that the endeavor of such a non-standard evaluation between the three (automatic data-generated, topic modelling, user analysis) approaches would introduce. Moreover, there are already existing works that compare fully automatically generated personas with traditional ones, and which have yielded results that have been discussed in this paper [47].…”

Section: Discussionmentioning

confidence: 99%

Data-Assisted Persona Construction Using Social Media Data

Spiliotopoulos

Margaris

Vassilakis

2020

BDCC

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User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers.

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Section: Discussionmentioning

confidence: 99%

Data-Assisted Persona Construction Using Social Media Data

Spiliotopoulos

Margaris

Vassilakis

2020

BDCC

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“…A practical limitation of APG is the need for manually acquiring facial pictures for the persona profiles (Salminen et al 2019a). Because the pictures for APG are acquired from online stock photo banks (e.g.…”

Section: Research Contextmentioning

confidence: 99%

“…Due its low cost, AIG provides novel opportunities for a wide range of applications, including health-care (Nie et al 2017), advertising (Neumann, Pyromallis, and Alexander 2018), and user analytics for human computer interaction (HCI) and design purposes (Salminen et al 2019a). However, despite the far-reaching interest in AIG among academia and across industries, there is scant research on evaluating the suitability of the generated images for practical use in deployed systems.…”

Section: Introductionmentioning

confidence: 99%

“…One of the important issues for automatically creating DDPs from data is the availability of persona picturessince DDP systems can create dozens of personas in near real time, there is a need for an inventory of pictures to use when rendering the personas for end users to view and interact with. Conceptually, this leads to a need for an AIG module that creates suitable persona pictures on demand (Salminen et al 2019a). However, prior to engaging in system development, there is a need for ensuring that artificially generated pictures are not detrimental to user perceptions of the personas, or otherwise, one risks futile efforts with immature technology.…”

Section: Introductionmentioning

confidence: 99%

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Using artificially generated pictures in customer-facing systems: an evaluation study with data-driven personas

Salminen

Jung

Kamel

et al. 2020

Behaviour & Information Technology

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We conduct two studies to evaluate the suitability of artificially generated facial pictures for use in a customer-facing system using data-driven personas. STUDY 1 investigates the quality of a sample of 1,000 artificially generated facial pictures. Obtaining 6,812 crowd judgments, we find that 90% of the images are rated medium quality or better. STUDY 2 examines the application of artificially generated facial pictures in data-driven personas using an experimental setting where the highquality pictures are implemented in persona profiles. Based on 496 participants using 4 persona treatments (2 × 2 research design), findings of Bayesian analysis show that using the artificial pictures in persona profiles did not decrease the scores for Authenticity, Clarity, Empathy, and Willingness to Use of the data-driven personas.

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“…We could locate no previous empirical analyses of the change in personas' topical interests in any context. We are interested in examining this aspect for personas in pursuit of a larger research project to make personas more data‐driven and reflective of audience analytics and segments (Jung, Salminen, An, Kwak, & Jansen, ; Salminen, Jansen, An, Kwak, & Jung, ).…”

Section: Introductionmentioning

confidence: 99%

Capturing the change in topical interests of personas over time

Jansen

Jung

Salminen

2019

Proceedings of the Association for Information Science and Tech

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In this research, we collect monthly content consumption and demographic data from YouTube over two years for a large media publisher. We use automation to generate 15 personas each month and examine the consistency of the generated personas over time. We find that there are 35 unique personas in total for the entire period, reflecting the changes in the underlying audience population. For each persona, we generate topics of interest and identify the top three monthly topics for each of the 35 personas following an identical algorithmic approach each month. We then compare the sets of topical interests of the personas month‐over‐month for the entire two‐year period. Findings show that there is an average 20.2% change in topical interests and that 68% of the personas experience more topical change than topical consistency. Findings suggest that the topical interests of online audiences are fluid and changes in the underlying audience data can occur within a relatively short period, resulting in the need for constant updating of personas using data‐driven methods. The implications for organizations seeking to understand their online audience are that they should employ routine data analysis to detect changes in the audience interests and investigate ways to automate their persona generation processes.

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Automatic Persona Generation for Online Content Creators: Conceptual Rationale and a Research Agenda

Cited by 21 publications

References 41 publications

Data-Assisted Persona Construction Using Social Media Data

Data-Assisted Persona Construction Using Social Media Data

Using artificially generated pictures in customer-facing systems: an evaluation study with data-driven personas

Capturing the change in topical interests of personas over time

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