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

被引:5
|
作者
Mancosu, Moreno [1 ]
Bobba, Giuliano [1 ,2 ]
机构
[1] Coll Carlo Alberto, Turin, Italy
[2] Univ Turin, Dept Cultures Polit & Soc, Turin, Italy
来源
PLOS ONE | 2019年 / 14卷 / 01期
关键词
POLITICAL-PARTICIPATION; FACEBOOK; ONLINE; TWITTER; AGE; ENGAGEMENT; TWEET; PREFERENCES; DIVIDE; VOTE;
D O I
10.1371/journal.pone.0211013
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:20
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