Selection bias is the bias introduced by the non random selection of data, it leads to question whether the sample obtained is representative of the target population. Generally there are different types of selection bias, but when one manages web-surveys or data from social network as Twitter or Facebook, one mostly need to focus with sampling and self-selection bias. In this work we propose to use offcial statistics to anchor and remove the sampling bias and unreliability of the estimations, due to the use of social network big data, following a weighting method combined with a small area estimations (SAE) approach.

A proposal to deal with sampling bias in social network big data

Porro G.;
2018-01-01

Abstract

Selection bias is the bias introduced by the non random selection of data, it leads to question whether the sample obtained is representative of the target population. Generally there are different types of selection bias, but when one manages web-surveys or data from social network as Twitter or Facebook, one mostly need to focus with sampling and self-selection bias. In this work we propose to use offcial statistics to anchor and remove the sampling bias and unreliability of the estimations, due to the use of social network big data, following a weighting method combined with a small area estimations (SAE) approach.
2018
2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018)
2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018)
València
July 12th-13th, 2018
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2079990
 Attenzione

L'Ateneo sottopone a validazione solo i file PDF allegati

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 2
social impact