Given the fast rise of increasingly autonomous artificial agents and robots, a key acceptability criterion will be the possible moral implications of their actions. In particular, intelligent persuasive systems (systems designed to influence humans via communication) constitute a highly sensitive topic because of their intrinsically social nature. Still, ethical studies in this area are rare and tend to focus on the output of the required action. Instead, this work focuses on the persuasive acts themselves (e.g. "is it morally acceptable that a machine lies or appeals to the emotions of a person to persuade her, even if for a good end?"). Exploiting a behavioral approach, based on human assessment of moral dilemmas -- i.e. without any prior assumption of underlying ethical theories -- this paper reports on a set of experiments. These experiments address the type of persuader (human or machine), the strategies adopted (purely argumentative, appeal to positive emotions, appeal to negative emotions, lie) and the circumstances. Findings display no differences due to the agent, mild acceptability for persuasion and reveal that truth-conditional reasoning (i.e. argument validity) is a significant dimension affecting subjects' judgment. Some implications for the design of intelligent persuasive systems are discussed.
Recently there has also been a growing interest in persuasive internet and mobile services, see the survey in @cite_4 @cite_6 . In parallel with this growth of application-oriented studies, there has been a growing interest in finding new cheap and fast' evaluation methodologies to assess effectiveness of persuasive communication by means of crowdsourcing approaches @cite_30 @cite_21 @cite_7 .
@cite_30 @cite_4 @cite_7 @cite_21 @cite_6
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In recent years there has been a growing interest in crowdsourcing methodologies to be used in experimental research for NLP tasks. In particular, evaluation of systems and theories about persuasion is difficult to accommodate within existing frameworks. In this paper we present a new cheap and fast methodology that allows fast experiment building and evaluation with fully-automated analysis at a low cost. The central idea is exploiting existing commercial tools for advertising on the web, such as Google AdWords, to measure message impact in an ecological setting. The paper includes a description of the approach, tips for how to use AdWords for scientific research, and results of pilot experiments on the impact of affective text variations which confirm the effectiveness of the approach. Persuasive communication is part of everyone's daily life. With the emergence of social websites like YouTube, Facebook and Twitter, persuasive communication is now seen online on a daily basis. This paper explores the effect of multi-modality and perceived personality on persuasiveness of social multimedia content. The experiments are performed over a large corpus of movie review clips from Youtube which is presented to online annotators in three different modalities: only text, only audio and video. The annotators evaluated the persuasiveness of each review across different modalities and judged the personality of the speaker. Our detailed analysis confirmed several research hypotheses designed to study the relationships between persuasion, perceived personality and communicative channel, namely modality. Three hypotheses are designed: the first hypothesis studies the effect of communication modality on persuasion, the second hypothesis examines the correlation between persuasion and personality perception and finally the third hypothesis, derived from the first two hypotheses explores how communication modality influence the personality perception. An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. We present a comprehensive study of evaluation methods for unsupervised embedding techniques that obtain meaningful representations of words from text. Different evaluations result in different orderings of embedding methods, calling into question the common assumption that there is one single optimal vector representation. We present new evaluation techniques that directly compare embeddings with respect to specific queries. These methods reduce bias, provide greater insight, and allow us to solicit data-driven relevance judgments rapidly and accurately through crowdsourcing.