When Digital Trace Data Meet Traditional Communication Theory: Theoretical/Methodological Directions. (February 2020)

• Record Type: Journal Article
• Authors: Choi, Sujin
• Other Names: Chen Wenhong guest-editor.; Quan-Haase Anabel guest-editor.
• Is Part Of: Social science computer review. Volume 38:Number 1(2020)
• Page Start: 91
• Page End: 107
• Publication Date: 2020-02
• Subjects: big data -- digital trace data -- computational social science -- communication theory -- text mining -- network analysis; Social sciences -- Data processing -- Periodicals; Computers -- Social aspects -- Periodicals; Microcomputers -- Periodicals; Sciences sociales -- Informatique -- Périodiques; Micro-ordinateurs -- Périodiques; 300.285
• DOI: 10.1177/0894439318788618
• Languages: English
• ISSNs: 0894-4393

Abstract

This study suggests one direction of theoretical and methodological coupling of communication research with the digital trace data, utilizing its differences from the traditional social science approach (e.g., sampling vs. population, normal distribution vs. power–law distribution, generalization vs. simulation, deductive vs. inductive, and perceived vs. actual). We propose specific examples of (i) combining communication research with trace data methodologically and theoretically; (ii) collaborating with linguistic psychology complemented with the automated content analysis and natural language processing techniques; and (iii) creating new theoretical inquiries by configuring the granular level of interactivity and underlying dynamics, observing the longitudinal change of interactions, and discovering the neglected presence of outliers and the invisibles. We expect the direction suggested by this study contributes to deepening our understanding of human communication behavior.