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A proposal to deal with sampling bias in social network big data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Iacus, Stefano Maria Porro, Giuseppe Salini, Silvia Siletti, Elena |
| Copyright Year | 2018 |
| 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. |
| Starting Page | 29 |
| Ending Page | 37 |
| Page Count | 9 |
| File Format | PDF HTM / HTML |
| DOI | 10.4995/CARMA2018.2018.8302 |
| Alternate Webpage(s) | http://ocs.editorial.upv.es/index.php/CARMA/CARMA2018/paper/viewFile/8302/4271 |
| Alternate Webpage(s) | https://doi.org/10.4995/CARMA2018.2018.8302 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
