Research on Data News Propagation Path Based on the Big Data Algorithm

• Published in: International transactions on electrical energy systems Vol. 2022; pp. 1 - 13
• Main Authors: Hao, Zhoucheng, Liu, Yanan, Wang, Gang
• Format: Journal Article
• Language: English
• Published: Hoboken Hindawi 2022; John Wiley & Sons, Inc; Wiley
• Subjects: Accuracy; Actor-network theory; Algorithms; Big Data; Clustering; Data analysis; Data mining; Datasets; Efficiency; False information; Hyperplanes; Identification; Liability; Methods; News; Paradigms; Pattern analysis; Propagation; Semantic analysis; Semantics; Social networks; Support vector machines; Time series
• ISSN: 2050-7038; 2050-7038
• DOI: 10.1155/2022/5600004

News propagation originates from a person/location, dwelling with an event that grabs significance. News data propagation relies on telecommunication and big data for precise content distribution and mitigation of false news. Considering these factors, the event-dependent data propagation technique (EDPT) was introduced to improve the data precision. These data refer to the news information originating and propagating from digital media. The data analysis considers the external factors for fake information and precise projection medium for preventing multiviewed false circulations. In this technique, the liability of the information is analyzed using a linear pattern support vector classifier. The data modification and propagation changes are classified based on liability information across the circulation time. The SVM classifier identifies these two factors with close liability validation, preventing false data. The data accumulation and analysis rates for the abovementioned classifications are performed in the propagation process using the classifier hyperplane. This plane is updated from the previous propagation point from which the events are identified. The proposed technique’s performance is analyzed using propagation accuracy, precision, false rate, time, and rate.