dc.date.accessioned | 2021-02-23T20:21:20Z | |
dc.date.available | 2021-02-23T20:21:20Z | |
dc.date.created | 2020-10-08T18:11:32Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Patone, Martina Zhang, Li Chun . On Two Existing Approaches to Statistical Analysis of Social Media Data. International Statistical Review. 2020 | |
dc.identifier.uri | http://hdl.handle.net/10852/83555 | |
dc.description.abstract | Using social media data for statistical analysis of general population faces commonly two basic obstacles: firstly, social media data are collected for different objects than the population units of interest; secondly, the relevant measures are typically not available directly but need to be extracted by algorithms or machine learning techniques. In this paper, we examine and summarise two existing approaches to statistical analysis based on social media data, which can be discerned in the literature. In the first approach, analysis is applied to the social media data that are organised around the objects directly observed in the data; in the second one, a different analysis is applied to a constructed pseudo survey dataset, aimed to transform the observed social media data to a set of units from the target population. We elaborate systematically the relevant data quality frameworks, exemplify their applications and highlight some typical challenges associated with social media data. | |
dc.language | EN | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | On Two Existing Approaches to Statistical Analysis of Social Media Data | |
dc.type | Journal article | |
dc.creator.author | Patone, Martina | |
dc.creator.author | Zhang, Li Chun | |
cristin.unitcode | 185,15,13,25 | |
cristin.unitname | Statistikk og Data Science | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 1838299 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=International Statistical Review&rft.volume=&rft.spage=&rft.date=2020 | |
dc.identifier.jtitle | International Statistical Review | |
dc.identifier.pagecount | 18 | |
dc.identifier.doi | https://doi.org/10.1111/insr.12404 | |
dc.identifier.urn | URN:NBN:no-86286 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0306-7734 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/83555/1/insr.12404.pdf | |
dc.type.version | PublishedVersion | |
cristin.articleid | insr.12404 | |
dc.relation.project | NFR/237718 | |