Abortion as a taboo topic: A network text analysis of abortion disclosure decisions and boundary coordination

This study extends the body of research on self-disclosure of taboo topics through the theoretical lens of Petronio's communication privacy management (CPM). When faced with an unintended pregnancy, women must make decisions about to whom they can reveal this potentially risky information. This study investigates and identifies the rule based process women use to decide when to disclose and when to remain private.;Two distinct research methodologies provide analysis of 2,000 pages and more than 60 hours of qualitative respondent interview text. First, interview text was analyzed using the artificial neural network software known as CATPAC to discover clusters of meaning represented in the interviews. Second, a more traditional, qualitative textual analysis was employed to uncover the rules of disclosure for each of the clusters identified by CATPAC. Analysis of who women chose to disclose to resulted in the identification of eight clusters of meaning and nine categories of rules for disclosure. Analysis of who women chose not to disclose to resulted in six clusters of meaning and 13 categories of rules for nondisclosure.;Results suggest that respondents primarily chose female confidants with whom they had a positive history of communication regarding taboo topics as well as knowledge of her previous experience with pregnancy, abortion or childbirth. Respondents kept this information private from any individuals in her life she believed would interfere with her choice or possibly perceive her negatively (i.e., impression management) as a result of her decision to terminate a pregnancy. Analysis revealed relationship labels, general and specific clusters of meaning, and disclosure rules that enrich the CPM literature. Complete results, practical implications and suggestions for future research are presented.