A recurrent criticism concerning the use of online social media data in political science research is the lack of demographic information about social media users. By employing a face-recognition algorithm to the profile pictures of Facebook users, the paper derives two fundamental demographic characteristics (age and gender) of a sample of Facebook users who interacted with the most relevant British parties in the two weeks before the Brexit referendum of 23 June 2016. The article achieves the goals of (i) testing the precision of the algorithm, (ii) testing its validity, (iii) inferring new evidence on digital mobilisation, and (iv) tracing the path for future developments and application of the algorithm. The findings show that the algorithm is reliable and that it can be fruitfully used in political and social sciences both to confirm the validity of survey data and to obtain information from populations that are generally unavailable within traditional surveys.

Using deep-learning algorithms to derive basic characteristics of social media users: The Brexit campaign as a case study

Mancosu M.;Bobba G.
2019-01-01

Abstract

A recurrent criticism concerning the use of online social media data in political science research is the lack of demographic information about social media users. By employing a face-recognition algorithm to the profile pictures of Facebook users, the paper derives two fundamental demographic characteristics (age and gender) of a sample of Facebook users who interacted with the most relevant British parties in the two weeks before the Brexit referendum of 23 June 2016. The article achieves the goals of (i) testing the precision of the algorithm, (ii) testing its validity, (iii) inferring new evidence on digital mobilisation, and (iv) tracing the path for future developments and application of the algorithm. The findings show that the algorithm is reliable and that it can be fruitfully used in political and social sciences both to confirm the validity of survey data and to obtain information from populations that are generally unavailable within traditional surveys.
2019
Inglese
Esperti anonimi
14
1
1
20
20
Adult; Female; Humans; Male; United Kingdom; Deep Learning; European Union; Image Processing, Computer-Assisted; Social Media
no
1 – prodotto con file in versione Open Access (allegherò il file al passo 6 - Carica)
262
2
Mancosu M.; Bobba G.
info:eu-repo/semantics/article
open
03-CONTRIBUTO IN RIVISTA::03A-Articolo su Rivista
File in questo prodotto:
File Dimensione Formato  
2019_PLOS.pdf

Accesso aperto

Tipo di file: PDF EDITORIALE
Dimensione 1.43 MB
Formato Adobe PDF
1.43 MB Adobe PDF Visualizza/Apri

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/2318/1768107
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 5
social impact