149 research outputs found
Multimodal Human Group Behavior Analysis
Human behaviors in a group setting involve a complex mixture of multiple modalities: audio, visual, linguistic, and human interactions. With the rapid progress of AI, automatic prediction and understanding of these behaviors is no longer a dream. In a negotiation, discovering human relationships and identifying the dominant person can be useful for decision making. In security settings, detecting nervous behaviors can help law enforcement agents spot suspicious people. In adversarial settings such as national elections and court defense, identifying persuasive speakers is a critical task. It is beneficial to build accurate machine learning (ML) models to predict such human group behaviors. There are two elements for successful prediction of group behaviors. The first is to design domain-specific features for each modality. Social and Psychological studies have uncovered various factors including both individual cues and group interactions, which inspire us to extract relevant features computationally. In particular, the group interaction modality plays an important role, since human behaviors influence each other through interactions in a group. Second, effective multimodal ML models are needed to align and integrate the different modalities for accurate predictions. However, most previous work ignored the group interaction modality. Moreover, they only adopt early fusion or late fusion to combine different modalities, which is not optimal. This thesis presents methods to train models taking multimodal inputs in group interaction videos, and to predict human group behaviors. First, we develop an ML algorithm to automatically predict human interactions from videos, which is the basis to extract interaction features and model group behaviors. Second, we propose a multimodal method to identify dominant people in videos from multiple modalities. Third, we study the nervousness in human behavior by a developing hybrid method: group interaction feature engineering combined with individual facial embedding learning. Last, we introduce a multimodal fusion framework that enables us to predict how persuasive speakers are.
Overall, we develop one algorithm to extract group interactions and build three multimodal models to identify three kinds of human behavior in videos: dominance, nervousness and persuasion. The experiments demonstrate the efficacy of the methods and analyze the modality-wise contributions
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Dialogue Systems Specialized in Social Influence: Systems, Methods, and Ethics
This thesis concerns the task of how to develop dialogue systems specialized in social influence and problems around deploying such systems. Dialogue systems have become widely adopted in our daily life. Most dialogue systems are primarily focused on information-seeking tasks or social companionship. However, they cannot apply strategies in complex and critical social influence tasks, such as healthy habit promotion, emotional support, etc. In this work, we formally define social influence dialogue systems to be systems that influence users’ behaviors, feelings, thoughts, or opinions through natural conversations. We also present methods to make such systems intelligible, privacy-preserving, and thus deployable in real life. Finally, we acknowledge potential ethical issues around social influence systems and propose solutions to mitigate them in Chapter 6.
Social influence dialogues span various domains, such as persuasion, negotiation, and recommendation. We first propose a donation persuasion task, PERSUASIONFORGOOD, and ground our study on this persuasion task for social good. We then build a persuasive dialogue system, by refining the dialogue model for intelligibility and imitating human experts for persuasiveness, and a negotiation agent that can play the game of Diplomacy by decoupling the planning engine and the dialogue generation module to improve controllability of social influence systems. To deploy such a system in the wild, our work examines how humans perceive the AI agent’s identity, and how their perceptions impact the social influence outcome. Moreover, dialogue models are trained on conversations, where people could share personal information. This creates privacy concerns for deployment as the models may memorize private information.
To protect user privacy in the training data, our work develops privacy-preserving learning algorithms to ensure deployed models are safe under privacy attacks. Finally, deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves. So we propose JUICER, a framework to make use of both binary and free-form textual human feedback to augment the training data and keep improving dialogue model performance after deployment. Building social influence dialogue systems enables us to research future expert-level AI systems that are accessible via natural languages, accountable with domain knowledge, and privacy-preserving with privacy guarantees
On the Promotion of the Social Web Intelligence
Given the ever-growing information generated through various online social outlets, analytical research on social media has intensified in the past few years from all walks of life. In particular, works on social Web intelligence foster and benefit from the wisdom of the crowds and attempt to derive actionable information from such data. In the form of collective intelligence, crowds gather together and contribute to solving problems that may be difficult or impossible to solve by individuals and single computers. In addition, the consumer insight revealed from social footprints can be leveraged to build powerful business intelligence tools, enabling efficient and effective decision-making processes. This dissertation is broadly concerned with the intelligence that can emerge from the social Web platforms. In particular, the two phenomena of social privacy and online persuasion are identified as the two pillars of the social Web intelligence, studying which is essential in the promotion and advancement of both collective and business intelligence.
The first part of the dissertation is focused on the phenomenon of social privacy. This work is mainly motivated by the privacy dichotomy problem. Users often face difficulties specifying privacy policies that are consistent with their actual privacy concerns and attitudes. As such, before making use of social data, it is imperative to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, we utilize user social footprints to detect their privacy preferences automatically. An unsupervised collaborative filtering approach is proposed to characterize the attributes of publicly available accounts that are intended to be private. Unlike the majority of earlier studies, a variety of social data types is taken into account, including the social context, the published content, as well as the profile attributes of users. Our approach can provide support in making an informed decision whether to exploit one\u27s publicly available data to draw intelligence.
With the aim of gaining insight into the strategies behind online persuasion, the second part of the dissertation studies written comments in online deliberations. Specifically, we explore different dimensions of the language, the temporal aspects of the communication, as well as the attributes of the participating users to understand what makes people change their beliefs. In addition, we investigate the factors that are perceived to be the reasons behind persuasion by the users. We link our findings to traditional persuasion research, hoping to uncover when and how they apply to online persuasion. A set of rhetorical relations is known to be of importance in persuasive discourse. We further study the automatic identification and disambiguation of such rhetorical relations, aiming to take a step closer towards automatic analysis of online persuasion. Finally, a small proof of concept tool is presented, showing the value of our persuasion and rhetoric studies
Proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET 2013)
"This book contains the proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET) 2013 which was held on 16.-17.September 2013 in Paphos (Cyprus) in conjunction with the EC-TEL conference. The workshop and hence the proceedings are divided in two parts: on Day 1 the EuroPLOT project and its results are introduced, with papers about the specific case studies and their evaluation. On Day 2, peer-reviewed papers are presented which address specific topics and issues going beyond the EuroPLOT scope. This workshop is one of the deliverables (D 2.6) of the EuroPLOT project, which has been funded from November 2010 – October 2013 by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission through the Lifelong Learning Programme (LLL) by grant #511633. The purpose of this project was to develop and evaluate Persuasive Learning Objects and Technologies (PLOTS), based on ideas of BJ Fogg. The purpose of this workshop is to summarize the findings obtained during this project and disseminate them to an interested audience. Furthermore, it shall foster discussions about the future of persuasive technology and design in the context of learning, education and teaching. The international community working in this area of research is relatively small. Nevertheless, we have received a number of high-quality submissions which went through a peer-review process before being selected for presentation and publication. We hope that the information found in this book is useful to the reader and that more interest in this novel approach of persuasive design for teaching/education/learning is stimulated. We are very grateful to the organisers of EC-TEL 2013 for allowing to host IWEPLET 2013 within their organisational facilities which helped us a lot in preparing this event. I am also very grateful to everyone in the EuroPLOT team for collaborating so effectively in these three years towards creating excellent outputs, and for being such a nice group with a very positive spirit also beyond work. And finally I would like to thank the EACEA for providing the financial resources for the EuroPLOT project and for being very helpful when needed. This funding made it possible to organise the IWEPLET workshop without charging a fee from the participants.
Analyzing the Persuasive Effect of Style in News Editorial Argumentation
News editorials argue about political issues in order to challenge or reinforce the stance of readers with different ideologies. Previous research has investigated such persuasive effects for argumentative content. In contrast, this paper studies how important the style of news editorials is to achieve persuasion. To this end, we first compare content- and style-oriented classifiers on editorials from the liberal NYTimes with ideology-specific effect annotations. We find that conservative readers are resistant to NYTimes style, but on liberals, style even has more impact than content. Focusing on liberals, we then cluster the leads, bodies, and endings of editorials, in order to learn about writing style patterns of effective argumentation
The persuasiveness of humanlike computer interfaces varies more through narrative characterization than through the uncanny valley
Indiana University-Purdue University Indianapolis (IUPUI)Just as physical appearance affects persuasion and compliance in human communication, it may also bias the processing of information from avatars, computer-animated characters, and other computer interfaces with faces. Although the most persuasive of these interfaces are often the most humanlike, they incur the greatest risk of falling into the uncanny valley, the loss of empathy associated with eerily human characters. The uncanny valley could delay the acceptance of humanlike interfaces in everyday roles. To determine the extent to which the uncanny valley affects persuasion, two experiments were conducted online with undergraduates from Indiana University. The first experiment (N = 426) presented an ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (recorded or animated), motion quality (smooth or jerky), and recommendation (disclose or refrain from disclosing sensitive information). Of these, only the recommendation changed opinion about the dilemma, even though the animated depiction was eerier than the human depiction. These results indicate that compliance with an authority persists even when using a realistic computer-animated double. The second experiment (N = 311) assigned one of two different dilemmas in professional ethics involving the fate of a humanlike character. In addition to the dilemma, there were three manipulations of the character’s human realism: depiction (animated human or humanoid robot), voice (recorded or synthesized), and motion quality (smooth or jerky). In one dilemma, decreasing depiction realism or increasing voice realism increased eeriness. In the other dilemma, increasing depiction realism decreased perceived competence. However, in both dilemmas realism had no significant effect on whether to punish the character. Instead, the willingness to punish was predicted in both dilemmas by narratively characterized trustworthiness. Together, the experiments demonstrate both direct and indirect effects of narratives on responses to humanlike interfaces. The effects of human realism are inconsistent across different interactions, and the effects of the uncanny valley may be suppressed through narrative characterization
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Citation Networks, Linguistics-Based Cues, and Logic-Based Approaches to Understanding What Persuades a Judge to Forsake Bias
Questions regarding what persuades jurists—and how legal decisionmakers actually do their work—are profound, motivating, and complex. The Public Law subfield has worked diligently to obtain empirically principled answers, but the gaps that remain provide an opportunity for this project to (hopefully) make a contribution. After discussing the nature of judicial decisionmaking, it is reasoned that rather than trying to understand jurists based upon the ways that their biases come into their work, a more effective approach is to isolate the occasions where they make unbiased decisions. In the interest of furthering the argument, a theoretical framework is offered that aims to isolate the major factors that will influence a jurist to “follow the law.”
After a review of the state of the empirical study of judicial decisionmaking, three subprojects are presented, two of which tie directly to terms in the theoretical framework. The first is a novel effort to construct a network of case citations based upon specific language used in majority opinions. The second examines the propensity of Supreme Court Justices to cite to more “central” opinions when they are tending towards moderation in terms of ideology. The third subproject focuses on the often overlooked difficulty that scholars have when attempting to state with definitive certainty what an “unbiased” legal opinion actually is.
These three subprojects are modest efforts to open new directions in research. Not all of the results that have been obtained fully square with the theoretical expectations that preceded them
The Diffusion of a Personal Health Record for Patients with Type 2 Diabetes Mellitus in Primary Care
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Three essays on the study of nationalization with automated content analysis
In three papers, I consider two questions of nationalization in American politics, and one question of the methodology necessary to study them.
Nationalization is the process by which local politics become more like national politics on the basis of political issues and electoral engagement. It is usually measured using the difference in presidential and state-level electoral returns over time. To expand the study of nationalization, I use automated content analysis to derive new measures for the phenomenon’s study based on political text. In particular, I apply automated content analysis via latent dirichlet allocation to code for salient topics in text from national political agenda speech, local agenda speech, and state laws. The primary source for these local agenda codes is a novel database of State of the State addresses, which are like presidential State of the Union addresses, but are delivered by governors. I developed the database over the past seven years as part of this dissertation; it draws from all 50 States, and the earliest captured addresses date to the year 1893. The secondary sources for these codes are the State of the Union addresses and a corpus of laws passed by state legislatures. I utilize the codes from these naturally distinct text corpora to study the nationalization of the political agenda, and how nationalized elections relate to the production of salient laws. The comparison of naturally distinct texts, however, is problematic and requires further examination. To that end, the first paper, “A Theory and Method for Pooling Naturally Distinct Corpora” concerns the theory and method for why we should be able to use, pool, and compare the computer-generated codes from these naturally distinct text corpora to study nationalization. I propose a theoretical framework with which the researcher may defend the pooling of corpora, and introduce an empirical approach to testing for absolute comparability, the delta-statistic. While statistics like the Akaike Information Criterion (AIC) and penalized log likelihood can help the researcher to determine if a model fits the pooled corpora better than the corpora separately, the delta-statistic relies on a strong theory of latent traits to evaluate the absolute quality of a pooled model. This is especially important when it is impossible to evaluate ground truth fit because some data are unlabeled.
The second paper, “Have State Policy Agendas Become More Nationalized?” examines whether the nationalization of state policy agendas is related to the nationalization of gubernatorial elections. The analysis shows that State agendas, as laid out in the State of the State addresses, have become more similar to each other over time. It also shows that State agendas have become more similar to the national agenda, as laid out in the State of the Union addresses. Finally, I demonstrate an increasing relationship between the similarity in the agenda and the nationalization of elections. The findings suggest that the nationalization of the agenda is a significant and related factor to the nationalization of elections.
The third paper, “Can States Govern Effectively When Politics Are Nationalized?” considers the question of whether electoral nationalization moderates the relationship between divided government and legislative productivity in the states. I find a null effect of divided government on salient lawmaking ability, and that nationalization of state legislatures has generally decreased the production of salient laws. The result holds even though nationalization is unrelated to the ability of our state governments to take action on salient issues during times of divided government. The findings suggest that behavioral factors driving lawmaker decisions may be more to blame for lawmaking defects than institutional ones.
Taken together, the essays demonstrate the value of text analysis to the analysis of nationalization and other research topics in American politics
The Pandemic of Argumentation
This open access book addresses communicative aspects of the current COVID-19 pandemic as well as the epidemic of misinformation from the perspective of argumentation theory. Argumentation theory is uniquely placed to understand and account for the challenges of public reason as expressed through argumentative discourse. The book thus focuses on the extent to which the forms, norms and functions of public argumentation have changed in the face of the COVID-19 pandemic. This question is investigated along the three main research lines of the COST Action project CA 17132: European network for Argumentation and Public PoLicY analysis (APPLY): descriptive, normative, and prescriptive. The volume offers a broad range of contributions which treat argumentative phenomena that are directly related to the changes in public discourse in the wake of the outburst of COVID-19. The volume additionally places particular emphasis on expert argumentation, given (i) the importance expert discourse has had over the last two years, and (ii) the challenges that expert argumentation has faced in the public sphere as a result of scientific uncertainty and widespread misinformation. Contributions are divided into three groups, which (i) examine various features and aspects of public and institutional discourse about the COVID-19 pandemic, (ii) scrutinize the way health policies have been discussed, debated, attacked and defended in the public sphere, and (iii) consider a range of proposals meant to improve the quality of public discourse, and public deliberation in particular, in such a way that concrete proposals for argumentative literacy will be brought to light. Overall, this volume constitutes a timely inquiry into all things argumentative in pandemic discourse. This volume is of interest to a broad readership including philosophers, linguists, communication and legal scholars, and members of the wider public who seek to better understand the discourse surrounding communicative phenomena in times of crisis. COST (European Cooperation in Science and Technology) is a funding organisation for research and innovation networks. For more information: www.cost.e
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