Analysis of Deception Detection Databases Using Mathematical Statistics
Omirali A. Zhumaliyeva R. Bayazitov D. Kozhakhmet K.
2024Natural Sciences Publishing
Applied Mathematics and Information Sciences
2024#18Issue 61273 - 1280 pp.
With the rise of digital media platforms and social networks, distinguishing between trustworthy and false information has become increasingly complicated. This has posed challenges in shaping public opinion and making informed decisions. In order to tackle this issue, this research paper presents a novel dataset based on experimental findings, which is specifically designed for detecting factual and fabricated statements in video content. The dataset was carefully curated through the production of independent videos in diverse scenarios, capturing both honest and deceitful contexts. The paper provides a thorough description of the methodologies used in collecting, processing, extracting features, and annotating the data, highlighting its credibility and representativeness. In addition, the paper offers a comprehensive analysis of existing databases in the deception detection tasks, underscoring the significance of this new dataset.
Data annotation , Dataset creation , Deception detection , Digital media , Feature extraction , Information authenticity , Machine learning models , Social networks , True and false statements , Video materials
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School of Digital Technologies, Narxoz University, Almaty, Kazakhstan
School of Digital Technologies
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