He is a real weasel: Interpreting metaphors modified by stance marking adjectives and adverbs in English

Languages have various lexical and grammatical means of expressing attitudes, assessment, and emotions, known, among other terms, as stance markers. In English, adjectives and adverbs like 'real' and 'really' are common examples of stance markers which have an intensifying effect on the propositional content of a message (e.g. He is a real good person sometimes). Although this intensificational effect has been well explored in the use of literal language, little research has looked at its impact on figurative language. In the present study, we investigate how stance markers influence the interpretation of X is Y metaphors in English (e.g. He is a weasel). More specifically, we present two novel experiments, one including stance marking adjectives (e.g. He is a real weasel) and the other stance marking adverbs (e.g. He is really a weasel). Our goal is to understand how people interpret metaphoric statements both in the presence and absence of stance markers, also in cases of negated statements, where the relevant features of the metaphor source are weakened: (1) He is a weasel. [non-negated, non-intensified] (2) He is a real weasel. [non-negated, intensified] (3) He is not a weasel. [negated, non-intensified] (4) He is not a real weasel. [negated, intensified] (1) He is a weasel. [non-negated, non-intensified] (2) He is really a weasel. [non-negated, intensified] (3) He is not a weasel. [negated, non-intensified] (4) He is not really a weasel. [negated, intensified

Artificial Intelligence in Public Discourse

This book contains 26 studies conducted by students in the Cognitive Science seminar "Artificial Intelligence in Public Discourse". In their studies, they explore the use of the term Artificial Intelligence (AI) and related subfields in various parts of public discourse such as Twitter, user comments on news sites, expert interviews, government documents, television shows, newspapers, etc. It is investigated which strengths, weaknesses, opportunities, and threats are ascribed to AI technology and how this relates to the technical and academic state of the art and discussion. Most studies employ qualitative methods, but quantitative and mixed-methods approaches are also used