Identifier	Thesis 1996
Author	Spomer, Judith E.
Title	Latent semantic analysis and classification modeling in applications for social movement theory
Publisher	Central Connecticut State University
Date	2009
Resource Type	Master's Thesis
Notes	Social Movement Theory (SMT) is an area of study in Sociology and Political Science that provides an analytical framework for understanding the factors involved in organized social action. A social movement develops in response to an injustice or issue about which people rally in an effort to solve the problem. In recent years, the threat of terrorism has accelerated research in SMT. Much of this research has focused on understanding the framing process, whereby a Social Movement Organization (SMO) issues communications intended to influence perceptions and enlist help from the members of a community or general population. The Internet has become a primary medium for SMOs to distribute electronic text to describe an issue, place blame, identify victims, propose solutions, and ask readers to take action on an issue. Texts such as these are framing documents. The research presented in this paper introduces the application of statistical methods in text analytics as a means to extend research involving the framing process. This thesis proposes that Latent Semantic Analysis techniques combined with classification modeling algorithms results in models that are able to discover small numbers of framing documents scattered among thousands of text documents. The models themselves provide insight into the character of framing documents. Global warming was selected as the social movement upon which to base this study. Global warming framing documents were collected from Internet sites, and were combined with other documents that address global warming, but are not framing in nature. This corpus served to train and test statistical models that not only detected framing documents, but further classified these by framing task with high accuracy. These methods can be implemented with commercial software and serve as a resource for the study of both SMT and active social movements.
Subject	Latent semantic indexing Data mining
Department	Department of Mathematical Sciences
Advisor	Bilisoly, Roger, 1963-
Type	Text
Digital Format	application/pdf
Language	eng
OCLC number	713734985
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Latent semantic analysis and classification modeling in applications for social movement theory

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