|
|
 |
| |
|
|
|
Analysis of Deception Detection Databases Using Mathematical Statistics |
|
|
|
PP: 1273-1280 |
|
|
|
doi:10.18576/amis/180609
|
|
|
|
Author(s) |
|
|
|
Aikumis Omirali,
Rakhima Zhumaliyeva,
Didar Bayazitov,
Kanat Kozhakhmet,
|
|
|
|
Abstract |
|
|
| 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.
|
|
|
|
|
|
|
|
