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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
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