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• W2913016613 startingPage "e0211013" @default.
• W2913016613 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." @default.
• W2913016613 created "2019-02-21" @default.
• W2913016613 creator A5023019752 @default.
• W2913016613 creator A5054814270 @default.
• W2913016613 date "2019-01-25" @default.
• W2913016613 modified "2023-10-18" @default.
• W2913016613 title "Using deep-learning algorithms to derive basic characteristics of social media users: The Brexit campaign as a case study" @default.
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• W2913016613 doi "https://doi.org/10.1371/journal.pone.0211013" @default.
• W2913016613 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6347201" @default.
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