Diesner, J., Lewis, E.T., & Carley, K.M. (2001). Using Automated Text Analysis to Study Self-Presentation Strategies. Computational Analysis of Social and Organizational Systems (CASOS) Conference, Pittsburgh PA, 2001. [.pdf]

Abstract:
Extracting and representing the networks of ties between concepts in a set of texts creates a “map” of each text. Using map analysis, a researcher systematically reduces the content of texts, then extracts and compares the networks of ties between concepts. In this paper we will present map analysis results that attempt to capture the self-presentation strategies authors use in their texts. (Managing issues of self-presentation is a central goal of many different types of texts.) Our research focuses on the implications that different coding and data reduction techniques have for interpreting map analysis networks. We use an automated text analysis program (AutoMap) to extract the concepts in the text, link them into statements based on their proximity in the text, and then into networks of statements within the entire text. The texts we study are a set of applications on behalf of entrepreneurs for an “Entrepreneur of the Year” award. The authors use a finite set of concepts in their texts, but arrange them in different combinations depending on the specific strategic intent of the text. Applicants value uniqueness in their 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 strategic accounts is not as clear. We found that using even a minimally rhetorically informed rule to form statements improves the interpretability of concept networks by eliminating redundancy and creating networks that reflect strong ties between concepts.