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Giving Faces to Data: Creating Data-Driven Personas from Personified Big Data

Published: 17 March 2020 Publication History

Abstract

Creating personas from large amounts of online data is useful but difficult with manual methods. To address this difficulty, we present Automatic Persona Generation (APG), which is an implementation of a methodology for quantitatively generating data-driven personas from online social media data. APG is functional, and it is deployed with several organizations in multiple industry verticals. APG employs a scalable web front-end user interface and robust back-end database framework processing tens of millions of user interactions with tens of thousands of online digital products across multiple online platforms, including Facebook, Google Analytics, and YouTube. APG identifies audience segments that are both distinct and impactful for an organization to create persona profiles. APG enhances numerical social media data with relevant human attributes, such as names, photos, topics, etc. Here, we discuss the architecture development and central system features. Overall, APG can benefit organizations distributing content via online platforms or with online content that relates to commercial products. APG is unique in its algorithmic approach to processing social media data for customer insights. APG can be found online at https://persona.qcri.org.

References

[1]
An, J. et al. 2018. Imaginary People Representing Real Numbers: Generating Personas from Online Social Media Data. ACM Transactions on the Web (TWEB). 12, 4 (2018), Article No. 27.
[2]
Jung, S. et al. 2018. Automatically Conceptualizing Social Media Analytics Data via Personas. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2018) (San Francisco, California, USA, Jun. 2018).
[3]
Jung, S. et al. 2017. Persona Generation from Aggregated Social Media Data. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (Denver, Colorado, USA, 2017), 1748--1755.
[4]
Kumar, A. et al. 2020. Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Information Processing & Management. 57, 1 (2020), 102141.
[5]
Lee, D.D. and Seung, H.S. 1999. Learning the parts of objects by non-negative matrix factorization. Nature. 401, 6755 (Oct. 1999), 788--791.

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  • (2023)Research on the Application of Public Cultural Service APP Design Based on AI Technology2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)10.1109/ACCTCS58815.2023.00045(224-227)Online publication date: Feb-2023
  • (2023)How Can Natural Language Processing and Generative AI Address Grand Challenges of Quantitative User Personas?HCI International 2023 – Late Breaking Papers10.1007/978-3-031-48057-7_14(211-231)Online publication date: 23-Jul-2023
  • (2023)Data-Driven Persona Creation, Validation, and EvolutionRequirements Engineering: Foundation for Software Quality10.1007/978-3-031-29786-1_18(262-271)Online publication date: 17-Apr-2023
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        cover image ACM Conferences
        IUI '20 Companion: Companion Proceedings of the 25th International Conference on Intelligent User Interfaces
        March 2020
        153 pages
        ISBN:9781450375139
        DOI:10.1145/3379336
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Publication History

        Published: 17 March 2020

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        Author Tags

        1. Personas
        2. data-driven personas
        3. persona development

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        Cited By

        View all
        • (2023)Research on the Application of Public Cultural Service APP Design Based on AI Technology2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)10.1109/ACCTCS58815.2023.00045(224-227)Online publication date: Feb-2023
        • (2023)How Can Natural Language Processing and Generative AI Address Grand Challenges of Quantitative User Personas?HCI International 2023 – Late Breaking Papers10.1007/978-3-031-48057-7_14(211-231)Online publication date: 23-Jul-2023
        • (2023)Data-Driven Persona Creation, Validation, and EvolutionRequirements Engineering: Foundation for Software Quality10.1007/978-3-031-29786-1_18(262-271)Online publication date: 17-Apr-2023
        • (2022)Survey2Persona: Rendering Survey Responses as PersonasAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3536403(67-73)Online publication date: 4-Jul-2022
        • (2022)Developing Persona Analytics Towards Persona ScienceProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511144(323-344)Online publication date: 22-Mar-2022
        • (2022)Persona preparedness: a survey instrument for measuring the organizational readiness for deploying personasInformation Technology and Management10.1007/s10799-022-00373-925:2(173-198)Online publication date: 13-Sep-2022
        • (2022)Candidate Personas im Unternehmen etablierenDas Persona-Prinzip10.1007/978-3-658-38979-6_10(95-106)Online publication date: 30-Oct-2022
        • (2022)Getting Your Organization Data-Driven Persona ReadyData-Driven Personas10.1007/978-3-031-02231-9_2(29-60)Online publication date: 8-Mar-2022
        • (2022)Data-Driven PersonasundefinedOnline publication date: 8-Mar-2022
        • (2021)Persona Analytics: Implementing Mouse-Tracking for an Interactive Persona SystemExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451773(1-8)Online publication date: 8-May-2021
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