Deception Detection

Deception detection research aims to automatically identify deceptive statements or behaviors across various modalities (text, audio, video), focusing on improving accuracy and understanding the underlying mechanisms of deception. Current research employs diverse machine learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs and BiLSTMs), transformers, and support vector machines (SVMs), often combined in multimodal approaches to leverage information from multiple sources. This field is crucial for addressing ethical and security concerns in areas like online content moderation, fraud detection, and law enforcement, with ongoing efforts to create robust, generalizable, and explainable deception detection systems.