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Using deep-learning algorithms to derive basic characteristics of social media users: The Brexit campaign as a case
Moreno Mancosu1, Giuliano Bobba1,2
1Collegio Carlo Alberto, Turin, Italy.
Plos One
|January 26, 2019
Summary
This study uses a face-recognition algorithm to determine the age and gender of Facebook users interacting with British political parties. The reliable algorithm offers new insights into digital mobilization and can supplement survey data in political science research.
Area of Science:
- Political Science
- Computational Social Science
- Social Media Analytics
Background:
- Political science research often lacks demographic data for online social media users.
- Traditional surveys face challenges in capturing data from certain online populations.
Purpose of the Study:
- To assess the precision and validity of a face-recognition algorithm for deriving user demographics.
- To infer new evidence on digital mobilization surrounding the Brexit referendum.
- To explore future applications of such algorithms in social science research.
Main Methods:
- Utilized a face-recognition algorithm applied to Facebook profile pictures.
- Analyzed users interacting with major British political parties before the 2016 Brexit referendum.
- Derived demographic characteristics: age and gender.
Main Results:
- The face-recognition algorithm demonstrated reliability and precision.
- Successfully inferred demographic data for a sample of Facebook users.
- Provided new evidence on digital mobilization patterns.
Conclusions:
- The face-recognition algorithm is a valuable tool for political and social sciences.
- It can validate survey data and provide insights into previously inaccessible populations.
- Offers a promising avenue for future research in digital demographics and political behavior.
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