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
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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.