Artificial neural networks applied for predicting and explaining the education level of Twitter users

• Published in: Social network analysis and mining 2021-12, Vol.11 (1), p.112-112, Article 112
• Main Authors: Florea, Alexandru Razvan, Roman, Monica
• Format: Article
• Language: English
• Subjects: Accuracy; Acknowledgment; Applications of Graph Theory and Complex Networks; Artificial intelligence; Artificial neural networks; Computer Science; COVID-19 vaccines; Data Mining and Knowledge Discovery; Data sources; Digital media; Economics; Education; Face recognition; Game Theory; Humanities; Law; Methodology of the Social Sciences; Neural networks; Neurons; Original; Original Article; Pattern recognition; Social and Behav. Sciences; Social media; Social networks; Social studies; Sociodemographics; Statistics for Social Sciences
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-021-00832-1

This paper provides a novel procedure to estimate the education level of social network (SN) users by leveraging artificial neural networks (ANN). Additionally, it provides a robust methodology to extract explanatory insights from ANN models. It also contributes to the study of socio-demographic phenomena by utilizing less explored data sources, such as social media. It proposes Twitter data as an alternative data source for in-depth social studies, and ANN for complex patterns recognition. Moreover, cutting edge technology, such as face recognition, on social media data are applied to explain the social characteristics of country-specific users. We use nine variables and three hidden layers of neurons to identify high-skilled users. The resulted model describes well the level of education by correctly estimating it with an accuracy of 95% on the training set and an accuracy of 92% on a testing set. Approximately 30% of the analyzed users are highly skilled and this share does not differ among the two genders. However, it tends to be lower among users younger than 30 years old.