We describe a computational model of social norms based on identifying values that a certain culture finds desirable such as dignity, generosity and politeness. The model quantifies these values in the form of Culture-Sanctioned Social Metrics (CSSMs) and treats social norms as the requirement to maximize these metrics from the perspective of the self, peers and public. This model can be used to create realistic social simulations, to explain or predict human behavior in specific scenarios, or as a component of robots or agents that need to interact with humans in specific social-cultural settings. We validate the model by using it to represent a complex deception scenario and showing that it can yield non-trivial insights such as the explanation of apparently irrational human behavior.
Another approach to modeling social-cultural behavior is through computational linguistics. For instance, @cite_11 extract social power relationships from the language used by the speakers, relying on words and features which can be identified by natural language processing software. Danescu-Niculescu- @cite_28 used computational linguistics to determine the type of politeness strategies (in the Brown-Levinson sense) used by Wikipedia authors in their communication logs.
@cite_28 @cite_11
2151667087 2130703925 1506679937 2530299326
A central source of cultural differences is the communication of politeness, which can powerfully affect perception and behavior. Our computational adaptation of a universal theory of human politeness (from Brown and Levinson, 1987) combines culture-specific aspects of social context to generate expectations, score, interpret and recommend polite and impolite behaviors. Tests show this approach could reduce software development costs and increase an intelligent agent's behavior repertoire through the incorporation of modular, cross-cultural etiquette libraries. Synchronized verbal behavior can reveal important information about social dynamics. This study introduces the linguistic style matching (LSM) algorithm for calculating verbal mimicry based on an automated textual analysis of function words. The LSM algorithm was applied to language generated during a small group discussion in which 70 groups comprised of 324 individuals engaged in an information search task either face-to-face or via text-based computer-mediated communication. As a metric, LSM predicted the cohesiveness of groups in both communication environments, and it predicted task performance in face-to-face groups. Other language features were also related to the groups’ cohesiveness and performance, including word count, pronoun patterns, and verb tense. The results reveal that this type of automated measure of verbal mimicry can be an objective, efficient, and unobtrusive tool for predicting underlying social dynamics. In total, the study demonstrates the effectiveness of using language to predi... We argue that traditional cultural factors models (from Hofstede, Nisbett, etc.) are too abstract to provide good predictions of important human performance behaviors such as directive compliance. Instead, we focus on culture-specific social interaction behaviors (i.e., "etiquette") as a bridge between abstract cultural factors and human performance. We describe a computational model of etiquette and politeness perception, called CECAEDA (Computational Effects of Cultural Attributes and Etiquette on Directive Adherence). CECAEDA consists of four parts: (1) a culturally universal model of politeness perceptions, their causes and effects, (2) a culturally universal cognitive model compliance decision making and behaviors, (3) a set of hypotheses about how politeness perceptions alter directive compliance, and (4) a set of hypotheses about how cultural factors (specifically, those proposed by Hofstede [1]) affect etiquette perceptions and, thus, directive compliance in culture-specific ways. Each component is discussed in detail, followed by a brief presentation of our research test bed and paradigm for evaluating CECAEDA. We present an interpretable neural network approach to predicting and understanding politeness in natural language requests. Our models are based on simple convolutional neural networks directly on raw text, avoiding any manual identification of complex sentiment or syntactic features, while performing better than such feature-based models from previous work. More importantly, we use the challenging task of politeness prediction as a testbed to next present a much-needed understanding of what these successful networks are actually learning. For this, we present several network visualizations based on activation clusters, first derivative saliency, and embedding space transformations, helping us automatically identify several subtle linguistics markers of politeness theories. Further, this analysis reveals multiple novel, high-scoring politeness strategies which, when added back as new features, reduce the accuracy gap between the original featurized system and the neural model, thus providing a clear quantitative interpretation of the success of these neural networks.