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Security issues of news data dissemination in internet environment
Journal of cloud computing : advances, systems and applications, 2024-12, Vol.13 (1), p.1-13
[Peer Reviewed Journal]
The Author(s) 2024 ;EISSN: 2192-113X ;DOI: 10.1186/s13677-024-00632-w
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Title:
Security issues of news data dissemination in internet environment
Author:
Song, Kang
;
Shang, Wenqian
;
Zhang, Yong
;
Yi, Tong
;
Wang, Xuan
Subjects:
CNN
;
Computer Communication Networks
;
Computer Science
;
Computer System Implementation
;
Computer Systems Organization and Communication Networks
;
Deep learning
;
GRU
;
Information Systems Applications (incl.Internet)
;
Rumor detection
;
Security and privacy issues for artificial intelligence in edge-cloud computing
;
Software Engineering/Programming and Operating Systems
;
Special Purpose and Application-Based Systems
;
User characteristics
Is Part Of:
Journal of cloud computing : advances, systems and applications, 2024-12, Vol.13 (1), p.1-13
Description:
With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment.
Publisher:
Berlin/Heidelberg: Springer Berlin Heidelberg
Language:
English
Identifier:
EISSN: 2192-113X
DOI: 10.1186/s13677-024-00632-w
Source:
Open Access: DOAJ Directory of Open Access Journals
SpringerOpen
AUTh Library subscriptions: ProQuest Central
ROAD
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