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A Data Quality Multidimensional Model for Social Media Analysis

Author

Listed:
  • María José Aramburu

    (Universitat Jaume I)

  • Rafael Berlanga

    (Universitat Jaume I)

  • Indira Lanza-Cruz

    (Universitat Jaume I)

Abstract

Social media platforms have become a new source of useful information for companies. Ensuring the business value of social media first requires an analysis of the quality of the relevant data and then the development of practical business intelligence solutions. This paper aims at building high-quality datasets for social business intelligence (SoBI). The proposed method offers an integrated and dynamic approach to identify the relevant quality metrics for each analysis domain. This method employs a novel multidimensional data model for the construction of cubes with impact measures for various quality metrics. In this model, quality metrics and indicators are organized in two main axes. The first one concerns the kind of facts to be extracted, namely: posts, users, and topics. The second axis refers to the quality perspectives to be assessed, namely: credibility, reputation, usefulness, and completeness. Additionally, quality cubes include a user-role dimension so that quality metrics can be evaluated in terms of the user business roles. To demonstrate the usefulness of this approach, the authors have applied their method to two separate domains: automotive business and natural disasters management. Results show that the trade-off between quantity and quality for social media data is focused on a small percentage of relevant users. Thus, data filtering can be easily performed by simply ranking the posts according to the quality metrics identified with the proposed method. As far as the authors know, this is the first approach that integrates both the extraction of analytical facts and the assessment of social media data quality in the same framework.

Suggested Citation

  • María José Aramburu & Rafael Berlanga & Indira Lanza-Cruz, 2024. "A Data Quality Multidimensional Model for Social Media Analysis," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(6), pages 667-689, December.
    • Handle: RePEc:spr:binfse:v:66:y:2024:i:6:d:10.1007_s12599-023-00840-9
    DOI: 10.1007/s12599-023-00840-9
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    References listed on IDEAS

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    1. Ruojing Zhang & Marta Indulska & Shazia Sadiq, 2019. "Discovering Data Quality Problems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(5), pages 575-593, October.
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    4. Sadiq, Shazia & Indulska, Marta, 2017. "Open data: Quality over quantity," International Journal of Information Management, Elsevier, vol. 37(3), pages 150-154.
    5. Lee, In, 2018. "Social media analytics for enterprises: Typology, methods, and processes," Business Horizons, Elsevier, vol. 61(2), pages 199-210.
    6. Rafael Berlanga & Lisette García-Moya & Victoria Nebot & María José Aramburu & Ismael Sanz & Dolores María Llidó, 2015. "SLOD-BI: An Open Data Infrastructure for Enabling Social Business Intelligence," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 11(4), pages 1-28, October.
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