IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v39y2024i1d10.1007_s00180-022-01261-0.html
   My bibliography  Save this article

Missing values and data enrichment: an application to social media liking

Author

Listed:
  • Paolo Mariani

    (University of Milano-Bicocca)

  • Andrea Marletta

    (University of Milano-Bicocca)

  • Matteo Locci

    (University of Milano-Bicocca)

Abstract

In the big data context, it is very frequent to manage the analysis of missing values. This is especially relevant in the field of statistical analysis, where this represents a thorny issue. This study proposes a strategy for data enrichment in presence of sparse matrices. The research objective consists in the evaluation of a possible distinction of behaviour among observations in sparse matrices with missing data. After selecting among the multiple imputation methods, an innovative technique will be presented to impute missing observations as a negative position or a neutral opinion. This method has been applied to a dataset measuring the interaction between users and social network pages for some Italian newspapers.

Suggested Citation

  • Paolo Mariani & Andrea Marletta & Matteo Locci, 2024. "Missing values and data enrichment: an application to social media liking," Computational Statistics, Springer, vol. 39(1), pages 217-237, February.
  • Handle: RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-022-01261-0
    DOI: 10.1007/s00180-022-01261-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-022-01261-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00180-022-01261-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Crosato, Lisa & Domenech, Josep & Liberati, Caterina, 2021. "Predicting SME’s default: Are their websites informative?," Economics Letters, Elsevier, vol. 204(C).
    2. Paolo Mariani & Andrea Marletta & Mauro Mussini & Mariangela Zenga & Erika Grammatica, 2020. "A missing value approach to social network data: “Dislike” or “Nothing”?," Computational Management Science, Springer, vol. 17(4), pages 569-583, December.
    3. Julie Josse & Marie Chavent & Benot Liquet & François Husson, 2012. "Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis," Journal of Classification, Springer;The Classification Society, vol. 29(1), pages 91-116, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Husson, François & Josse, Julie & Saporta, Gilbert, 2016. "Jan de Leeuw and the French School of Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 73(i06).
    2. Johané Nienkemper-Swanepoel & Michael J Maltitz, 2017. "Investigating the Performance of a Variation of Multiple Correspondence Analysis for Multiple Imputation in Categorical Data Sets," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 384-398, October.
    3. Seán Schmitz & Sophia Becker & Laura Weiand & Norman Niehoff & Frank Schwartzbach & Erika von Schneidemesser, 2019. "Determinants of Public Acceptance for Traffic-Reducing Policies to Improve Urban Air Quality," Sustainability, MDPI, vol. 11(14), pages 1-16, July.
    4. Josse, Julie & Husson, François, 2016. "missMDA: A Package for Handling Missing Values in Multivariate Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i01).
    5. Kellard, Neil M. & Kontonikas, Alexandros & Lamla, Michael J. & Maiani, Stefano & Wood, Geoffrey, 2023. "Institutional settings and financing green innovation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    6. Cimmino, Francesco & Mastelic, Joelle & Genoud, Stephane, 2016. "Multi-Method Approach to Compare the Socio-Demographic Typology of Residents and Clusters of Electricity Load Curves in a Swiss Sustainable Neighbourhood," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2016), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 8-9 September 2016, pages 310-314, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    7. Zakaria Babutsidze & Marco Guerzoni & Luigi Riso, 2024. "Time varying effects in survival analysis: a novel data-driven method for drift identification and variable selection," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 14(1), pages 285-318, March.
    8. Christophe Biernacki & Matthieu Marbac & Vincent Vandewalle, 2021. "Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 129-157, April.
    9. Vincent Audigier & Ndèye Niang, 2023. "Clustering with missing data: which equivalent for Rubin’s rules?," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 623-657, September.
    10. Turner, Rachel A. & Szaboova, Lucy & Williams, Gwynedd, 2018. "Constraints to healthcare access among commercial fishers," Social Science & Medicine, Elsevier, vol. 216(C), pages 10-19.
    11. Fithian, William & Josse, Julie, 2017. "Multiple correspondence analysis and the multilogit bilinear model," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 87-102.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-022-01261-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.