Social media research

Highlights • Supervised Machine Learning (SML) is suitable for coding social media content. • Linear Support Vector Machine and Naïve Bayes classifiers can be trained using 4000 training tweets. • SML enables researchers to escalate the scope of their research without compromising data size or depth. • Linear Support Vector Machine and Naïve Bayes outperform the logistic regression classifier. • Classifiers perform better based on stratified random samples compared to random samples when training samples are small. Abstract Despite the online availability of data, analysis of this information in academic research is arduous. This article explores the application of supervised machine learning (SML) to overcome challenges associated with online data analysis. In SML classifiers are used to categorize and code binary data. Based on a case study of Dutch employees’…

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