Security Control Technology and Simulation of Network News Communication under the Environment of Internet of Things

Internet of Things is an application of network news communication technology. Based on the Internet, it uses physical access technologies such as radio frequency tag and wireless sensor network news communication and network news communication information transmission technology to build a network news communication information system that can cover people and things. In the physical layer, the relative position of the object is calculated by using multipoint cooperative localization, so as to determine the minimum anonymous region. Generate and maintain the anonymous tree topology on the network news dissemination layer, and provide storage management support for multiple anonymous groups. In the application layer, the object determines the corresponding anonymity degree according to the identity and uses the frame structure to construct and return the new anonymous group consistent with the existing anonymous group, which can prevent the persistent multiprecision query attack. A real-time control method for intrusion response of a security control system is designed, which includes two stages: response task generation and integrated scheduling. An intrusion response task set generation method based on an improved nondominated sorting genetic algorithm is presented, and a distributed integrated task scheduling and optimization algorithm based on a genetic algorithm and a directed acyclic graph is designed. The numerical simulation results show that this method can quickly and smoothly implement the response strategy of information security intrusion without affecting the normal execution of system tasks.

News Text Classification Method and Simulation Based on the Hybrid Deep Learning Model

This paper uses the database as the data source, using bibliometrics and visual analysis methods, to statistically analyze the relevant documents published in the field of text classification in the past ten years, to clarify the development context and research status of the text classification field, and to predict the research in the field of text classification priorities and research frontiers. Based on the in-depth study of the background, research status, related theories, and developments of online news text classification, this article analyzes the annual publication trend, subject distribution, journal distribution, institution distribution, author distribution, highly cited literature analysis, and research hotspots. Forefront and other aspects clarify the development context and research status of the text classification field and provide a theoretical reference for the further development of the text classification field. Then, on the basis of systematic research on text classification, deep learning, and news text classification theories, a deep learning-based network news text classification model is constructed, and the function of each module is introduced in detail, which will help the future news text classification of application and improvement provide theoretical basis. On the basis of the predecessors, this article separately studied and improved the neural network model based on the convolutional neural network, cyclic neural network, and attention mechanism and merged the three models into one model, which can obtain local associated features and contextual features and highlight the role of keywords. Finally, experiments are used to verify the effectiveness of the model proposed in this paper and compared with traditional text classification to prove the superiority of the network news text classification based on deep learning proposed in this paper. This article aims to study the internal connection between news comments and the number of votes received by news comments, and through the proposed model, the number of votes for news comments can be predicted.

Web News Data Extraction Technology Based on Text Keywords

In order to shorten the time for users to query news on the Internet, this paper studies and designs a network news data extraction technology, which can obtain the main news information through the extraction of news text keywords. Firstly, the TF-IDF keyword extraction algorithm, TextRank keyword extraction algorithm, and LDA keyword extraction algorithm are analyzed to understand the keyword extraction process, and the TF-IDF algorithm is optimized by Zipf’s law. By introducing the idea of model fusion, five schemes based on waterfall fusion and parallel combination fusion are designed, and the effects of the five schemes are verified by experiments. It is found that the designed extraction technology has a good effect on network news data extraction. News keyword extraction has a great application prospect, which can provide the basis for the research fields of news key phrases, news abstracts, and so on.

The Effect of MAOA Gene × Gender on the Relationship between Negative Network News Browsing Preference and Aggression among College Students

The present research aims to examine whether and how the negative network news browsing preference (NNNBP) affect individual’s aggression. Two studies were conducted in the current research: study 1 developed a new measurement scale—network news browsing preference questionnaire(NNBPQ). The results indicated it had appropriate reliability (Cronbach’s α = 0.93) and validity (χ 2 /df = 1.92, CFI = 0.94, NFI = 0.89, GFI = 0.89，TLI = 0.94，RMSEA = 0.05) , which was conformed to psychometrics standards, and could be used for further study. Study 2 explored the relationship between NNNBP and aggression, and the moderate effect of MAOA gene × gender among 352 college students. The results indicated that: (a) NNNBP could positively predict male college students’ hostility and total score. It also could positively predict female college students’ physical aggression, verbal aggression, hostility and total score. (b) MAOA Gene × gender had moderate effect on the relationship between NNNBP and aggression; more specially, NNNBP could positively predict female G allele carriers’ physical aggression and hostility, while didn’t show significant under other conditions. Finally, the limitations and future prospects of the present research were discussed.

Politicization and Polarization in COVID-19 News Coverage

This study examines the level of politicization and polarization in COVID-19 news in U.S. newspapers and televised network news from March to May 2020. Using multiple computer-assisted content analytic approaches, we find that newspaper coverage is highly politicized, network news coverage somewhat less so, and both newspaper and network news coverage are highly polarized. We find that politicians appear in newspaper coverage more frequently than scientists, whereas politicians and scientists are more equally featured in network news. We suggest that the high degree of politicization and polarization in initial COVID-19 coverage may have contributed to polarization in U.S. COVID-19 attitudes.