CASOS Working PAPER

"Using Automated Text Analysis to Study Self-Presentation Strategies" (PDF file)
Authors: Eleanor T. Lewis, Jana Diesner, and Kathleen M. Carley

Abstract
Extracting and representing the networks of ties between concepts in a set of texts creates a "map" of each text. Map analysis allows a researcher to compare the networks of ties between concepts in these texts by systematically reducing their content. The goals of this research paper are to answer both a methodological and a substantive question. First, how do the choices a researcher makes about how to generate maps using an automated text program alter the results, and how do these results compare to the results of hand-coding? Second, how can we interpret the results of map analysis to better understand the strategies authors use to manage their self-presentation, a central purpose of many texts. The texts we use are a subsample of a dataset of applications by entrepreneurs for an "Entrepreneur of the Year" award. Applicants value uniqueness in ther application's content because it sets them apart and demonstrates their worthiness for the award, but the value placed on uniqueness in the structure of their accounts is not as clear. Our analysis allows us to extract four general self-presentation strategies: the prepared entreprener, the driven entrepreneur, the creative niche entrepreneur, and the humble entrepreneur (a single entrepreneur may employ multiple strategies).