User participation in knowledge update of expert systems
Information and Management
Deception Detection through Automatic, Unobtrusive Analysis of Nonverbal Behavior
IEEE Intelligent Systems
Human Problem Solving
Journal of Management Information Systems
A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication
Journal of Management Information Systems
Typing or messaging? Modality effect on deception detection in computer-mediated communication
Decision Support Systems
Following linguistic footprints: automatic deception detection in online communication
Communications of the ACM - Enterprise information integration: and other tools for merging data
A Statistical Language Modeling Approach to Online Deception Detection
IEEE Transactions on Knowledge and Data Engineering
Examining Trust in Information Technology Artifacts: The Effects of System Quality and Culture
Journal of Management Information Systems
Journal of Management Information Systems
Decision support for determining veracity via linguistic-based cues
Decision Support Systems
Online customers' cognitive differences and their impact on the success of recommendation agents
Information and Management
Judging the Credibility of Information Gathered from Face-to-Face Interactions
Journal of Data and Information Quality (JDIQ)
Border Security Credibility Assessments via Heterogeneous Sensor Fusion
IEEE Intelligent Systems
Identification of fraudulent financial statements using linguistic credibility analysis
Decision Support Systems
Technology Dominance in Complex Decision Making: The Case of Aided Credibility Assessment
Journal of Management Information Systems
Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues
ACM Transactions on Management Information Systems (TMIS)
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Historically, inaccurate credibility assessments have resulted in tremendous costs to businesses and to society. Recent research offers unobtrusive credibility assessment aids as a solution; however, the accuracy of these decision aids is inadequate, and users often resist accepting the aids' recommendations. We follow the principles of signal detection theory to improve the accuracy of recommendations in computer-aided credibility assessment by combining automated and participatory decision support. We also leverage participation in decision-making theory to explain and predict an increased acceptance of assessment aid recommendations when perceptual cues are elicited from users. Based on these two theories, we design and test a hybrid decision aid to perform automated linguistic analysis and to elicit and analyze perceptual cues from an observer. Results from a laboratory experiment indicate that decision aids that use linguistic and perceptual cues offer more accurate recommendations than aids that use only one type of cue. Automatic analysis of linguistic cues improved both the decision aid's recommendations and the users' credibility assessment accuracy. Challenging the generalizability of past findings, the elicitation of perceptual cues did not improve the decision aid's recommendations or the users' assessment accuracy. Elicitation of perceptual cues, however, did improve user acceptance of the decision aid's recommendations. These findings provide guidance for future development of credibility assessment decision aids.