LEADER 04311nam 2200481 450 001 996464518203316 005 20210406115010.0 010 $a3-030-54383-8 024 7 $a10.1007/978-3-030-54383-9 035 $a(CKB)4100000011751952 035 $a(DE-He213)978-3-030-54383-9 035 $a(MiAaPQ)EBC6474264 035 $a(PPN)253860407 035 $a(EXLCZ)994100000011751952 100 $a20210406d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDetecting trust and deception in group interaction /$fV. S. Subrahmanian, Judee K. Burgoon, Norah E. Dunbar, editors 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (X, 222 p. 47 illus., 42 illus. in color.) 225 1 $aTerrorism, Security, and Computation,$x2197-8778 311 $a3-030-54382-X 327 $aPart 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 -- . 330 $aThis book analyzes the multimodal verbal and nonverbal behavior of humans in both an artificial game, based on the well-known Mafia and Resistance games, as well as selected other settings. This book develops statistical results linking different types of facial expressions (e.g. smile, pursed lips, raised eyebrows), vocal features (e.g., pitch, loudness) and linguistic features (e.g., dominant language, turn length) with both unary behaviors (e.g. is person X lying?) to binary behaviors (Is person X dominant compared to person Y? Does X trust Y? Does X like Y?). In addition, this book describes machine learning and computer vision-based algorithms that can be used to predict deception, as well as the visual focus of attention of people during discussions that can be linked to many binary behaviors. It is written by a multidisciplinary team of both social scientists and computer scientists. Meetings are at the very heart of human activity. Whether you are involved in a business meeting or in a diplomatic negotiation, such an event has multiple actors, some cooperative and some adversarial. Some actors may be deceptive, others may have complex relationships with others in the group. This book consists of a set of 11 chapters that describe the factors that link human behavior in group settings and attitudes to facial and voice characteristics. Researchers working in social sciences (communication, psychology, cognitive science) with an interest in studying the link between human interpersonal behavior and facial/speech/linguistic characteristics will be interested in this book. Computer scientists, who are interested in developing machine learning and deep learning based models of human behavior in group settings will also be interested in purchasing this book. 410 0$aTerrorism, Security, and Computation,$x2197-8778 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 702 $aSubrahmanian$b V. S. 702 $aBurgoon$b Judee K. 702 $aDunbar$b Norah E. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464518203316 996 $aDetecting trust and deception in group interaction$92814860 997 $aUNISA