The field of aggressive language detection is developing quickly in Natural Language Processing. However, most of the work being done in this field is centered around explicitly aggressive language, whereas work exploring forms of implicitly aggressive language is much less prolific thus far. Further, there are many subcategories that are encompassed within the greater category of implicitly aggressive language, for example, condescending and patronizing language. This thesis focuses on the relatively new field of patronizing and condescending language (PCL) detection, specifically on expanding away from in-domain tasks that focus on either news or social media texts. Cross-domain patronizing and condescending language detection is as of today not a widely explored sub-field of Natural Language Processing. In this project, the aim to answer three main research questions: the first is to what extent do models trained to detect patronizing and condescending language in one domain, in this case social media texts and news publications, generalize to other domains. Secondly, we aim to make advances toward a baseline for balanced PCL datasets and compare performance across label distribution ratios. Thirdly, we aim to address the impact of a common feature in patronizing and condescending language datasets--the significant imbalance between negative and positive labels in the binary classification task. To this end, we aim to address the question of to what extent does the proportion between labels have an impact on the in-domain PCL classification task.  We find that the best performing model for the in-domain classification task is the Gradient Boosting classifier trained on an imbalanced dataset harvested from Reddit, which included both the post and the reply, with a ratio of 1:2 between positive and negative labels. In the cross-domain task, we find that the best performing model is an SVM trained on the balanced news dataset and evaluated on the balanced Reddit post and reply dataset. In the latter study, we show that it is possible to achieve competitive results using classical machine models on a nuanced, context-dependent binary classification task.

Some people without disabilities may use patronizing speech when they talk to people with cognitive disabilities. This study asked college-aged students without disabilities to evaluate patronizing speech toward people with cognitive disabilities. They randomly read either one of two vignettes; in one vignette a cashier with no disability used patronizing speech toward a customer with a cognitive disability, and in the other vignette a cashier with no disability used nonpatronizing speech toward a customer with a cognitive disability. The participants evaluated the patronizing speech as being significantly less professional, appropriate, and common than the nonpatronizing speech. They rated the cashier as feeling significantly more warm, supportive, and nurturing when s/he used patronizing speech, and the customer as feeling significantly less respect when spoken to through patronizing speech. Significantly more participants believed they would have spoken differently than the cashier when s/he used patronizing speech.

A large portion of the American public is overweight and many are classified as being obese.  Obesity and unhealthy eating behavior are partially related to the increase in our society""s consumption of foods away from home. Accordingly, the Food and Drug Administration (FDA) has suggested new menu labeling regulations to help educate customers on healthy items among menu selections. Few studies have tried to understand customers"" healthy eating behavior in restaurants. Therefore, the purpose of this study was to understand and to predict customers' healthy eating behavior in casual dining restaurants, using the theory of planned behavior and the health belief model. The results showed that attitude toward healthy eating behavior and subjective norm positively influenced intention to engage in healthy eating behavior in casual dining restaurants while perceived behavioral control did not. For healthy eating behavior in casual dining restaurants, perceived threat, self-efficacy, response to provision of nutrition information (cue to action) were significant predictors. However, perceived benefits and barriers were not statistically significant. Also, the study found that subjective nutrition knowledge influenced customers' response to provision of nutrition information whereas objective nutrition knowledge did not. Customers' healthy eating behavior positively influenced their willingness to patronize a restaurant that offers healthy menu items, which means that those who try to eat healthy menu items in casual dining restaurants are willing to revisit restaurants where healthy menu choices are available and to recommend the restaurants to others. Finally, this study generated socio-demographic profiles related to healthy eating behavior in casual dining restaurants and willingness to patronize a restaurant that provides healthy menu choices. The results revealed that education levels and BMI (Body Mass Index) status influenced customers' healthy eating behavior. Also, customers' willingness to patronize a restaurant that provides healthy menu items differed based on gender, marital status, and education levels. / Ph. D.