In this study, we aim to identify moments of rudeness between two individuals. In particular, we segment all occurrences of rudeness in conversations into three broad, distinct categories and try to identify each. We show how machine learning algorithms can be used to identify rudeness based on acoustic and semantic signals extracted from conversations. Furthermore, we make note of our shortcomings in this task and highlight what makes this problem inherently difficult. Finally, we provide next steps which are needed to ensure further success in identifying rudeness in conversations.
Identifying moments of rude behaviour is directly complemented by identifying moments of politeness. Recently, the authors in @cite_12 annotate written text found in online communities and find strong correlations between the user's level of politeness towards others and his her hierarchical rank in that community. A linguistically informed classifier achieves at least 83 key finding reinforces the idea that individuals are more likely to be polite when they perceive themselves to be near the bottom of some structured hierarchy. The politeness of a user's phrase is annotated using Amazon Mechanical Turk https: www.mturk.com .
@cite_12
2157163421 2530299326 2151667087 1996209594
We propose a computational framework for identifying linguistic aspects of politeness. Our starting point is a new corpus of requests annotated for politeness, which we use to evaluate aspects of politeness theory and to uncover new interactions between politeness markers and context. These findings guide our construction of a classifier with domain-independent lexical and syntactic features operationalizing key components of politeness theory, such as indirection, deference, impersonalization and modality. Our classifier achieves close to human performance and is effective across domains. We use our framework to study the relationship between politeness and social power, showing that polite Wikipedia editors are more likely to achieve high status through elections, but, once elevated, they become less polite. We see a similar negative correlation between politeness and power on Stack Exchange, where users at the top of the reputation scale are less polite than those at the bottom. Finally, we apply our classifier to a preliminary analysis of politeness variation by gender and community. 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. 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. Abstract This paper is a critical examination of the theory of linguistic politeness proposed by Brown and Levinson (1978, 1987). The paper focuses on the notion, fundamental to their theory, of ‘face’, and questions the universality of the proposed constituents of ‘face’ in the light of the Japanese language and culture. First, results from anthropological studies on Japanese society are presented to illustrate the discrepancy between Brown and Levinson's assumption and the Japanese notion of ‘face’, Secondly, linguistic evidence is given that Brown and Levinson's theory provides wrong predictions for Japanese politeness phenomena. Examples are drawn from formulaic expressions, honorifics and the verbs of giving and receiving. One conclusion from these observations is that a universal theory of linguistic politeness must take into account at a more fundamental level the cultural variability in the constituents of ‘face’.