Guest Editors
Prof. Dr. Long-Sheng Chen
Email: lschen@ntut.edu.tw
Affiliation: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei City, 106, Taiwan
Homepage:
Research Interests: data mining, social networking, quality management, customer relationship management
Assoc. Prof. Chih Ming Tsai
Email: cmtsai@ncut.edu.tw
Affiliation: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung City, 411, Taiwan
Homepage:
Research Interests: knowledge and information management, customer relationship management, marketing planning research, e-commerce and online communities
Assist. Prof. Chien-Chih Chen
Email: frick@ncut.edu.tw
Affiliation: Department of Information Management, National Chin-Yi University of Technology, Taichung City, 411, Taiwan
Homepage:
Research Interests: small data set learning, machine learning, artificial neural networks
Summary
The rise of fake news, amplified by social media and generative AI, poses a growing threat to public trust and information integrity. Traditional detection tools are increasingly inadequate against the scale and realism of AI-generated content.
This special issue aims to develop an interdisciplinary approach that integrates natural language processing, computer vision, network analytics, and behavioral science into a comprehensive detection system. The scope focuses on the detection and mitigation strategies in a real-world environment where the disinformation tactics are constantly evolving and adapting.
The following subtopics are suggested, but not limited to:
1. Interdisciplinary Integration for Comprehensive Detection:
-Novel combinations of natural language processing, computer vision, network analytics, and behavioral science for false content detection.
-Synergistic approaches utilize multiple modalities for enhanced detection accuracy.
-Frameworks and methodologies for integrating diverse data sources and analytical techniques.
2. Technical Characteristics of False Content Analysis:
-Advanced NLP techniques for identifying linguistic cues of misinformation and disinformation.
-Computer vision methods for detecting manipulated or fabricated visual content.
-Network analysis of information spread patterns and identification of influential actors.
3. Human Factors in Consumption and Spread:
-Psychological and cognitive biases influencing susceptibility to false information.
-Social and behavioral dynamics driving the sharing and amplification of misinformation.
-Modeling user behavior and information consumption patterns in online environments.
4. Development of Comprehensive Detection Systems:
-Architectures and frameworks for building integrated detection systems.
-Evaluation metrics and benchmarks for assessing the performance of multi-modal detection systems.
-Scalability and efficiency considerations for real-world deployment.
5. Real-World Detection and Mitigation Strategies:
-Case studies and applications of interdisciplinary approaches in real-world scenarios.
-Strategies for mitigating the spread and impact of disinformation.
-Techniques for adapting to evolving disinformation tactics.
6. Evolving Disinformation Tactics:
-Analysis of emerging trends and novel techniques in the creation and dissemination of false information.
-Methods for detecting and countering sophisticated and adaptive disinformation campaigns.
-Understanding the interplay between technological advancements and evolving disinformation strategies.
Keywords
Fake News Detection, Social Media, Generative AI, Natural Language Processing, Computer Vision, Network Analytics, Behavioral Science
Published Papers
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