A Deep Learning-based Fast Fake News Detection Model for Cyber-Physical Social Services

Zhang, Qin; Guo, Zhiwei; Zhu, Yanyan; Vijayakumar, Pandi; Castiglione, Aniello; Gupta, Brij B.

doi:10.1016/j.patrec.2023.02.026

Cited by 114 publications

(27 citation statements)

References 21 publications

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“…A fast fake news detection model was proposed in [ 33 ]. This approach is particularly focused on cyber–physical social services.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Combating Misinformation in Multicategorical Text Contents

Kozik,

Mazurczyk,

Cabaj

et al. 2023

Sensors

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Currently, one can observe the evolution of social media networks. In particular, humans are faced with the fact that, often, the opinion of an expert is as important and significant as the opinion of a non-expert. It is possible to observe changes and processes in traditional media that reduce the role of a conventional ‘editorial office’, placing gradual emphasis on the remote work of journalists and forcing increasingly frequent use of online sources rather than actual reporting work. As a result, social media has become an element of state security, as disinformation and fake news produced by malicious actors can manipulate readers, creating unnecessary debate on topics organically irrelevant to society. This causes a cascading effect, fear of citizens, and eventually threats to the state’s security. Advanced data sensors and deep machine learning methods have great potential to enable the creation of effective tools for combating the fake news problem. However, these solutions often need better model generalization in the real world due to data deficits. In this paper, we propose an innovative solution involving a committee of classifiers in order to tackle the fake news detection challenge. In that regard, we introduce a diverse set of base models, each independently trained on sub-corpora with unique characteristics. In particular, we use multi-label text category classification, which helps formulate an ensemble. The experiments were conducted on six different benchmark datasets. The results are promising and open the field for further research.

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“…A fast fake news detection model was proposed in [ 33 ]. This approach is particularly focused on cyber–physical social services.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Combating Misinformation in Multicategorical Text Contents

Kozik,

Mazurczyk,

Cabaj

et al. 2023

Sensors

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“…The activity patterns of users in social networks play a pivotal role in the spread of information, leading to the emergence of information cascades. Gaining a deeper understanding of the underlying mechanisms of information diffusion carries significant economic and social advantages, with applications in various fields, including fake news detection (Zhang et al 2023), viral marketing (Miller and Lammas 2010), and recommender system (Ko et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Multi-Scale Diffusion Prediction via Sequential Hypergraphs and Adversarial Learning

Jiao,

Chen,

Bao

et al. 2024

AAAI

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Information diffusion prediction plays a crucial role in understanding the propagation of information in social networks, encompassing both macroscopic and microscopic prediction tasks. Macroscopic prediction estimates the overall impact of information diffusion, while microscopic prediction focuses on identifying the next user to be influenced. While prior research often concentrates on one of these aspects, a few tackle both concurrently. These two tasks provide complementary insights into the diffusion process at different levels, revealing common traits and unique attributes. The exploration of leveraging common features across these tasks to enhance information prediction remains an underexplored avenue. In this paper, we propose an intuitive and effective model that addresses both macroscopic and microscopic prediction tasks. Our approach considers the interactions and dynamics among cascades at the macro level and incorporates the social homophily of users in social networks at the micro level. Additionally, we introduce adversarial training and orthogonality constraints to ensure the integrity of shared features. Experimental results on four datasets demonstrate that our model significantly outperforms state-of-the-art methods.

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“…Before proceeding with the emergency situation, organizations should determine whether the emergency news is real or not. Zhang et al (2023) introduced a rapid fake news detection model for cyber-physical social services that employs deep learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Semantic-Rich Recommendation System for Medical Emergency Response System

Karthika,

Deborah,

Zheng

et al. 2024

International Journal on Semantic Web and Information Systems

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The emergency response process consists of methodical and coordinated series of actions and protocols executed by individuals and organizations to proficiently address crises. When planning for medical emergencies, it is vital to work with responsive medical organizations to ensure good communication and coordination. Unlike e-government processes, emergency response processes are focused on knowledge and may frequently change as the emergency situation develops. It is important to change the emergency response plan for dynamic situations and the proposed method helps to create a flexible plan for emergency responses. The proposed approach uses a system for organizing knowledge to figure out the needs and the resources essential for an emergency. It helps to identify the organizations to be involved based on their rules for mutual aid and jurisdiction. Experimental analysis shows that the proposed method outperforms Smart-c and DCERP in suggesting a greater number of hospitals during medical emergency and achieves 0.8, 0.9 and 0.9 precision, recall, and f-measure approximately.

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A Deep Learning-based Fast Fake News Detection Model for Cyber-Physical Social Services

Cited by 114 publications

References 21 publications

Deep Learning for Combating Misinformation in Multicategorical Text Contents

Deep Learning for Combating Misinformation in Multicategorical Text Contents

Enhancing Multi-Scale Diffusion Prediction via Sequential Hypergraphs and Adversarial Learning

Semantic-Rich Recommendation System for Medical Emergency Response System

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