Detecting trust and deception in group interaction [electronic resource]
- 其他作者：
- 其他題名：
- Terrorism, security, and computation.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Terrorism, security, and computation,
- 主題： Machine learning. , Pattern recognition systems. , Human behavior--Data processing. , Machine Learning. , Computer Science, general. , Media and Communication. , Image Processing and Computer Vision.
- ISBN： 9783030543839 (electronic bk.) 、 9783030543822 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Part I: Theory Underlying Investigating Deception in Groups -- 1. Prelude: Relational Communication and the link to Deception -- 2 An integrated Spiral Model of Trust -- 3. The Impact of Culture in Deception and Deception Detection -- Part II: The SCAN Project -- 4. A System for Multi-Person, Multi-Modal Data Collection in Behavioral Information Systems -- 5. Dominance in Groups: How Dyadic Power Theory Can Apply to Group Discussions -- 6. Behavioral Indicators of Dominance in an Adversarial Group Negotiation Game -- 7. Attention-based Facial Behavior Analytics in Social Communication -- 8. Iterative Collective Classification for Visual Focus of Attention Prediction -- Part III: SCAN Project Foundations: Preceding Empirical Investigations of Deception -- 9. Effects of Modality Interactivity and Deception Communication Quality and Task Performance -- 10. Incremental Information Disclosure in Qualitative Financial Reporting: Differences between Fraudulent and Non-Fraudulent Companies -- 11. Cultural Influence on Deceptive Communication.
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讀者標籤：
- 系統號： 005543891 | 機讀編目格式
館藏資訊
This book analyzes the multimodal verbal and nonverbal behavior of humans in both an artificial game, based on the well-known Mafia and Resistance games, as well as selected other settings. This book develops statistical results linking different types of facial expressions (e.g. smile, pursed lips, raised eyebrows), vocal features (e.g., pitch, loudness) and linguistic features (e.g., dominant language, turn length) with both unary behaviors (e.g. is person X lying?) to binary behaviors (Is person X dominant compared to person Y? Does X trust Y? Does X like Y?). In addition, this book describes machine learning and computer vision-based algorithms that can be used to predict deception, as well as the visual focus of attention of people during discussions that can be linked to many binary behaviors. It is written by a multidisciplinary team of both social scientists and computer scientists. Meetings are at the very heart of human activity. Whether you are involved in a business meeting or in a diplomatic negotiation, such an event has multiple actors, some cooperative and some adversarial. Some actors may be deceptive, others may have complex relationships with others in the group. This book consists of a set of 11 chapters that describe the factors that link human behavior in group settings and attitudes to facial and voice characteristics. Researchers working in social sciences (communication, psychology, cognitive science) with an interest in studying the link between human interpersonal behavior and facial/speech/linguistic characteristics will be interested in this book. Computer scientists, who are interested in developing machine learning and deep learning based models of human behavior in group settings will also be interested in purchasing this book.