Many social interactions rely on the premise of mutual trust, but deception violates trust and poses risk. Empirically examining trust and deception, particularly in high-stakes situations, is challenging but essential for improving the research realism and generalizability. To address this difficulty, we study trusting and deceptive behaviors in a high-stakes situation by using a novel dataset created from an American game show, Friend or Foe (FoF). In the show, a contestant's reward was determined through a trust game modified from the prisoner's dilemma. We explore how numerous human behaviors including facial expressions, gaze, head pose, body motion, language, and socio-demographic attributes, were related to a contestant's trusting or deceptive decision. Using a data-driven approach, we find that the deceivers' (contestants who chose Foe ) behavior featured a neutralized face, negative facial emotions, enhanced upper body motion, and language with a lower sense of immediacy and agreeableness. The contestants who chose to trust (chose Friend ) exhibited opposite behavioral patterns. Socio-demographic factors such as age, height, and facial attractiveness were also associated with a contestant's choice. Combining multimodal information, machine learning classifiers could predict the contestant's choice with an accuracy about 25% greater than earlier reported human accuracy. We contribute to both trust and deception literature by examining the generalizability of trusting and deceptive behaviors to a new high-stakes scenario. We also add to the decision support literature by showing the superior predictive performances of combining behavioral and socio-demographic features. Furthermore, we contribute to the academic community by introducing the FoF dataset.

Trust between group members has many implications for how well a group performs. In this study, we predict perceived trustworthiness of group members when there are subversive group members. We collected multimodal verbal and nonverbal data from a group interaction experiment. During the interaction, we periodically surveyed the group members about their perceptions of trustworthiness of other group members. We used this data to model the relationship between observable behavior and perceptions of trustworthiness. We report the most predictive features and describe them in the context of existing literature on verbal and nonverbal correlates of trust. This research advances the study of behavioral measurement in groups and the role of behavior on perceived trustworthiness.

Visual focus of attention in multi-person discussions is a crucial nonverbal indicator in tasks such as inter-personal relation inference, speech transcription, and deception detection. However, predicting the focus of attention remains a challenge because the focus changes rapidly, the discussions are highly dynamic, and the people's behaviors are inter-dependent. Here we propose ICAF (Iterative Collective Attention Focus), a collective classification model to jointly learn the visual focus of attention of all people. Every person is modeled using a separate classifier. ICAF models the people collectively---the predictions of all other people's classifiers are used as inputs to each person's classifier. This explicitly incorporates inter-dependencies between all people's behaviors. We evaluate ICAF on a novel dataset of 5 videos (35 people, 109 minutes, 7604 labels in all) of the popular Resistance game and a widely-studied meeting dataset with supervised prediction. See our demo at https://cs.dartmouth.edu/dsail/demos/icaf. ICAF outperforms the strongest baseline by 1%--5% accuracy in predicting the people's visual focus of attention. Further, we propose a lightly supervised technique to train models in the absence of training labels. We show that light-supervised ICAF performs similar to the supervised ICAF, thus showing its effectiveness and generality to previously unseen videos.

Conversational agents (CAs) are becoming an increasingly common component in a wide range of information systems. A great deal of research to date has focused on enhancing traits that make CAs more humanlike. However, few studies have examined the influence such traits have on information disclosure. This research builds on self-disclosure, social desirability, and social presence theories to explain how CA anthropomorphism affects disclosure of personally sensitive information. Taken together, these theories suggest that as CAs become more humanlike, the social desirability of user responses will increase. In this study, we use a laboratory experiment to examine the influence of two elements of CA design—conversational relevance and embodiment—on the answers people give in response to sensitive and non-sensitive questions. We compare the responses given to various CAs to those given in a face-to-face interview and an online survey. The results show that for sensitive questions, CAs with better conversational abilities elicit more socially desirable responses from participants, with a less significant effect found for embodiment. These results suggest that for applications where eliciting honest answers to sensitive questions is important, CAs that are “better” in terms of humanlike realism may not be better for eliciting truthful responses to sensitive questions.

Deception is pervasive, often leading to adverse consequences for individuals, organizations, and society. Information systems researchers are developing tools and evaluating sensors that can be used to augment human deception judgments. One sensor exhibiting particular promise is the eye tracker. Prior work evaluating eye trackers for deception detection has focused on the detection and interpretation of brief eye behavior variations in response to stimuli (e.g, images) or interview questions. However, research is needed to understand how eye behaviors evolve over the course of an interaction with a deception detection system. Using latent growth curve modeling, we test how pupil diameter evolves over one's interaction with a deception detection system. The results indicate that pupil diameter changes over the course of a deception detection interaction, and that these trends are indicative of deception during the interaction, regardless if incriminating target items are shown.

Automated human credibility screening is an emerging research area that has potential for high impact in fields as diverse as homeland security and accounting fraud detection. Systems that conduct interviews and make credibility judgments can provide objectivity, improved accuracy, and greater reliability to credibility assessment practices, need to be built. This study establishes a foundation for developing automated systems for human credibility screening.

This study is a quasi experiment comparing the software defect rates and implementation costs of two methods of software defect reduction: code inspection and test-driven development. We divided participants, consisting of junior and senior computer science students at a large Southwestern university, into four groups using a two-by-two, between-subjects, factorial design and asked them to complete the same programming assignment using either test-driven development, code inspection, both, or neither. We compared resulting defect counts and implementation costs across groups. We found that code inspection is more effective than test-driven development at reducing defects, but that code inspection is also more expensive. We also found that test-driven development was no more effective at reducing defects than traditional programming methods.

Transportation and border security systems have a common goal: to allow law-abiding people to pass through security and detain those people who intend to harm. Understanding how intention is concealed and how it might be detected should help in attaining this goal. In this paper, we introduce a multidisciplinary theoretical model of intent concealment along with three verbal and nonverbal automated methods for detecting intent: message feature mining, speech act profiling, and kinesic analysis. This paper also reviews a program of empirical research supporting this model, including several previously published studies and the results of a proof-of-concept study. These studies support the model by showing that aspects of intent can be detected at a rate that is higher than chance. Finally, this paper discusses the implications of these findings in an airport-screening scenario.

Creativity support tools is a research topic with high risk but potentially very high payoff. The goal is to develop improved software and user interfaces that empower users to be not only more productive but also more innovative. Potential users include software and other engineers, diverse scientists, product and graphic designers, architects, educators, students, and many others. Enhanced interfaces could enable more effective searching of intellectual resources, improved collaboration among teams, and more rapid discovery processes. These advanced interfaces should also provide potent support in hypothesis formation, speedier evaluation of alternatives, improved understanding through visualization, and better dissemination of results. For creative endeavors that require composition of novel artifacts (e.g., computer programs, scientific papers, engineering diagrams, symphonies, artwork), enhanced interfaces could facilitate exploration of alternatives, prevent unproductive choices, and enable easy backtracking. This U.S. National Science Foundation sponsored workshop brought together 25 research leaders and graduate students to share experiences, identify opportunities, and formulate research challenges. Two key outcomes emerged: (a) encouragement to evaluate creativity support tools through multidimensional in-depth longitudinal case studies and (b) formulation of 12 principles for design of creativity support tools.

Behavioral indicators of deception and behavioral state are extremely difficult for humans to analyze. This research effort attempts to leverage automated systems to augment humans in detecting deception by analyzing nonverbal behavior on video. By tracking faces and hands of an individual, it is anticipated that objective behavioral indicators of deception can be isolated, extracted and synthesized to create a more accurate means for detecting human deception. Blob analysis, a method for analyzing the movement of the head and hands based on the identification of skin color is presented. A proof-of-concept study is presented that uses Blob analysis to extract visual cues and events, throughout the examined videos. The integration of these cues is done using a hierarchical hidden Markov model to explore behavioral state identification in the detection of deception, mainly involving the detection of agitated and over-controlled behaviors

As a powerful and expressive nontextual media that can capture and present information, instructional videos are extensively used in e-learning (Web-based distance learning). Since each video may cover many subjects, it is critical for an e-learning environment to have content-based video searching capabilities to meet diverse individual learning needs. In this paper, we present an interactive multimedia-based e-learning environment that enables users to interact with it to obtain knowledge in the form of logically segmented video clips. We propose a natural language approach to content-based video indexing and retrieval to identify appropriate video clips that can address users' needs. The method integrates natural language processing, named entity extraction, frame-based indexing, and information retrieval techniques to explore knowledge-on-demand in a video-based interactive e-learning environment. A preliminary evaluation shows that precision and recall of this approach are better than those of the traditional keyword based approach.

This article describes the need for virtual workspaces and then discusses the architecture, design, and development of GroupSystems for the World Wide Web (Web) (GSWeb), an HTML/JavaScript Web-based Group Support System (GSS). To design and develop GSWeb, a user-driven approach was employed which drew on feedback from users and teams working with a GSWeb prototype, interviews with GSS users and facilitators, and over 10 years of experience with face-to-face group support research, development, and design. GSWeb was built using HTML 3.0 and JavaScript for client interface rendering. The result is an application interface that is like the familiar Graphical User Interface (GUI) interfaces that are today a defacto standard. GSWeb is currently being used by teams from all over the world, and continuing design and development still relies on feedback and requests from actual users to refine and extend the features that will enable virtual teams to be productive and accomplish real work. © 1998 John Wiley & Sons, Inc.

This article describes research in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. Electronic brainstorming is one of the most productive tools in the Electronic Meeting System called GroupSystems. A major step in group problem solving involves the classification of electronic brainstorming output into a manageable list of concepts, topics, or issues that can be further evaluated by the group. This step is problematic due to information overload and the cognitive demand of processing a large quantity of textual data. This research builds upon previous work in automating the meeting classification process using a Hopfield neural network. Evaluation of the Kohonen output comparing it with Hopfield and human expert output using the same set of data found that the Kohonen SOM performed as well as a human expert in representing term association in the meeting output and outperformed the Hopfield neural network algorithm. In addition, recall of consensus meeting concepts and topics using the Kohonen algorithm was equivalent to that of the human expert. However, precision of the Kohonen results was poor. The graphical representation of textual data produced by the Kohonen SOM suggests many opportunities for improving information organization of textual information. Increasing uses of electronic mail, computer-based bulletin board systems, and world-wide web services present unique challenges and opportunities for a system-aided classification approach. This research has shown that the Kohonen SOM may be used to automatically create “a picture that can represent a thousand (or more) words.” © 1997 John Wiley & Sons, Inc.

The applicability of a knowledge-based system for resource management in the context of information centers (ICs) is discussed. The Information Center Expert (ICE) system has been developed to support the consultation process of IC personnel. The system determines the (software) resource requirements of the end users and makes appropriate recommendations. ICE further aids the management of IC software resources by keeping track of user consultations and the recommendations made. Issues of knowledge requirements, acquisition and representation and implementation of ICE are discussed. An evaluation of ICEs which focused on maintainability and transportability was conducted at two corporate locations and a university setting. Based on the informal feedback, the implications of this approach for future research are discussed.>

Electronic Meeting Systems (EMS) are slowly moving out of university environments into work organizations. They constitute an innovative method of supporting group meetings. This paper reports on the innovation process in one organization that has recently adopted and implemented an EMS. The paper traces the innovation process through four stages: conception of an idea: proposal; decision to adopt; and implementation. Important factors from the innovation literature are considered as explanators of the innovation process involving EMS in this particular organization.

This research used a laboratory experiment to investigate the effects of group size on idea-generation performance and member satisfaction in an electronic meeting room in which computer-supported electronic communication replaced direct verbal communication. Three group sizes were studied: small (three-member), medium (nine-member) and large (18-member). The findings of this study contradict those of prior non-computer-supported idea-generation studies: in this electronic meeting environment, larger groups generated more ideas of greater quality, and were more satisfied than smaller groups.>

The use of integrated organization and information systems models in building and delivering business application systems is proposed. The concept of integrated organization and information systems modeling is discussed and illustrated by means of an extended critical success factors model. A flexible metasystem, MetaPlex, designed and implemented to support high-level organization and information systems modeling, is described in detail. The use of integrated models with executive information systems to support the delivery of information is discussed. Directions for future research are also suggested.>

A form model and an expert database system that analyzes instances of the form model to derive a conceptual schema are proposed. The form model describes the properties of form fields such as their origin, hierarchical structure, and cardinality. The expert database design system creates a conceptual schema by incrementally integrating related collections of forms. The rules of the expert systems are divided into six phases form selection; entity identification; attribute attachment; relationship identification; cardinality identification; and integrity constraints. The rules of the first phase use knowledge about the form flow to determine the order in which forms are analyzed. The rules in other phases are used in conjunction with a designer dialog to identify the entities, relationships, and attributes of a schema that represents the collection of forms.>

The increasing reliance on group decision-making in today's complex business environments and advances in microcomputer, telecommunications and graphic presentation technology have combined to create a growing interest in the design of group decision support systems (GDSS). Planning is an important group decision-making activity within organizations. Effective planning depends on the generation and analysis of innovative ideas. For this reason, the idea generation and management process has been chosen as the domain for the study of the design and implementation of a GDSS to support complex, unstructured group decision processes within organizations.The MIS Planning and Decision Laboratory has been constructed to provide a research facility for the study of the planning and decision process while top executives from a variety of organizations use the laboratory to conduct actual planning sessions for their organization. This paper presents the design of a system to support the idea generation and analysis process in organization planning. Results of research conducted in the MIS Planning and Decision Laboratory on the use of the Electronic Brainstorming system with over 100 planners from a variety of organizations are presented and discussed.The findings of the research indicate that computer brainstorming stimulates task oriented behavior, decreases group interactions and equalizes participation. Information presentation, network speed and typing skills of the upper level managers were identified as possible inhibitors of the idea generation process that must be considered in the design of the system and the methodology for its use. Planners using the GDSS reported high levels of satisfaction with the process and outcome of the planning sessions. They rated the computer as an important tool for idea generation and the computer brainstorming process as "Much Better" than manual brainstorming.