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1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences, Political science
Whilst the CIA have been using psychometric profiling for decades, Cambridge Analytica showed that people's psychological characteristics can be accurately predicted from their digital footprints, such as their Facebook or Twitter accounts. To exploit this form of psychological assessment from digital footprints, we propose machine learning methods for assessing political personality from Twitter. We have extracted the tweet content of Prime Minster Boris Johnson’s Twitter account and built three predictive personality models based on his Twitter political content. We use a Multi-Layer Perceptron Neural network, a Naive Bayes multinomial model and a Support Machine Vector model to predict the OCEAN model which consists of the Big Five personality factors from a sample of 3355 political tweets. The approach vectorizes political tweets, then it learns word vector representations as embeddings from spaCy that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the quality of the predictions for each trait per model from a classification algorithm. Our findings show that all three models compute the personality trait “Openness” with the Support Machine Vector model achieving the highest accuracy. “Extraversion” achieved the second highest accuracy personality score by the Multi-Layer Perceptron neural network and Support Machine Vector model.
Usher, James and Dondio, Pierpaolo, "BREXIT: Psychometric Profiling the Political Salubrious Through Machine Learning: Predicting personality traits of Boris Johnson through Twitter political text" (2020). Conference papers. 398.
No funding was received for this work.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.