Some syntax-only text feature extraction and analysis methods for social media data

Abstract

Automated characterization of online social behavior is becoming increasingly important as day-to-day human interaction migrates from expensive “real world” encounters to less expensive virtual interactions over computing networks. The effective automated characterization of human interaction in social media has important political, economic, social applications. New analytic concepts are presented for the extraction and enhancement of salient numeric features from unstructured text. These concepts employ relatively simple syntactic metrics for characterizing and distinguishing human and automated social media posting behaviors. The concepts are domain agnostic, and are empirically demonstrated using posted text from a particular social medium (Twitter). An innovation uses a feature-imputation regression method to perform feature sensitivity analysis.