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

Paolo Mariani (), Andrea Marletta () and Matteo Locci ()
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Paolo Mariani: University of Milano-Bicocca
Andrea Marletta: University of Milano-Bicocca
Matteo Locci: University of Milano-Bicocca

Computational Statistics, 2024, vol. 39, issue 1, No 12, 217-237

Abstract: 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.

Keywords: Missing values; Data enrichment; Multiple imputations; Social network data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01261-0

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