Predicting verbal reasoning from virtual community membership in a sample of Russian young adults.

ABSTRACT: Predicting personality traits from social networking site profiles can help to assess individual differences in verbal reasoning without using long questionnaires. Inspired by earlier studies, which investigated whether abstract-thinking ability are predictable by social networking sites data, we used supervised machine learning to predict verbal-reasoning ability based on a proposed set of features extracted from virtual community membership. A large sample (N = 3,646) of Russian young adults aged 18-22 years approved access to the data from their social networking accounts and completed an online test on verbal reasoning. We experimented with binary classification machine-learning models for verbal-reasoning prediction. Prediction performance was tested on isolated control subsamples for

SUBMITTER: Kiselev P 

PROVIDER: S-EPMC9198326 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature