Planning a Stigmatized Nonvisible Illness Disclosure: Applying the Disclosure Decision-Making Model

This study applied the disclosure decision-making model (DD-MM) to explore how individuals plan to disclose nonvisible illness (Study 1), compared to planning to disclose personal information (Study 2). Study 1 showed that perceived stigma from the illness negatively predicted disclosure efficacy; closeness predicted anticipated response (i.e., provision of support) although it did not influence disclosure efficacy; disclosure efficacy led to reduced planning, with planning leading to scheduling. Study 2 demonstrated that when information was considered to be intimate, it negatively influenced disclosure efficacy. Unlike the model with stigma (Study 1), closeness positively predicted both anticipated response and disclosure efficacy. The rest of the hypothesized relationships showed a similar pattern to Study 1: disclosure efficacy reduced planning, which then positively influenced scheduling. Implications of understanding stages of planning for stigmatized information are discussed.     

Data compression in machine learning applied to natural language

In this paper, we investigate the possibility of applying machine learning methods to data derived from the area of natural language and show how rules, induced by machine learning, are changed after the original data are compressed by grouping together entries, attributes, and attribute values. Also shown is how excessive compression of input data may affect the accuracy of induced rules.