Search

News dissemination is an important way for people to obtain information. With the development of new technologies, traditional news dissemination models have been impacted. It has problems with information filtering and bias, and has certain limitations in news quality, dissemination efficiency, etc., which makes it difficult to effectively meet people's information needs. In order to improve the quality and efficiency of news dissemination, promote the positive impact of news dissemination on society, this article combined artificial intelligence and big data technology to conduct in-depth research on the difficulties and coping strategies of news dissemination. This article first analyzed the characteristics and functions and influencing factors of news dissemination, then provided an overview of the difficulties and coping strategies in news dissemination. Finally, using association rule algorithms, personalized recommendations for news dissemination are achieved. To verify the effectiveness of artificial intelligence and big data in coping with the dilemma of news dissemination, this article conducted experimental analysis from the perspectives of news content quality, dissemination efficiency, objectivity, and dissemination cost. The experimental results show that under the application of news dissemination strategies based on artificial intelligence and big data, the quality of news content and dissemination efficiency have been improved by 4.76 % and 3.63 %, respectively. The conclusion indicates that artificial intelligence and big data can help improve the quality and dissemination efficiency of news content, and meet the diverse needs of the public for information.

The application of Internet of Things (IoT) technology in news media communication has significantly enhanced the effectiveness and coverage of news data releases. However, as the scale of news data continues to grow, traditional IoT approaches face challenges such as slow data processing speed and low mining efficiency. To address these issues, a novel news feature mining system combining IoT and Artificial Intelligence (AI) has been developed. The hardware components of the system include a data collector, a data analyzer, a central controller, and sensors. The GJ-HD data collector is utilized to gather news data. Multiple network interfaces are designed at the device terminal to ensure data extraction from the internal disk in case of device failure. The central controller integrates the MP/MC and DCNF interfaces for seamless information interconnection. In the software aspect of the system, the network transmission protocol of the AI algorithm is embedded, and a communication feature model is constructed. This enables fast and accurate mining of news data communication features. Experimental results demonstrate that the system achieves a mining accuracy of over 98%, enabling efficient processing of news data. Overall, the proposed IoT and AI-based news feature mining system overcomes the limitations of traditional approaches, allowing for efficient and accurate processing of news data in a rapidly expanding digital landscape.

With the continuous improvement of algorithm technology, the field of news communication has also ushered in a transformation from traditional media to self-media, from self-media to algorithmic media, and from the center of the transmitter to the center of the recipient. The news algorithm recommendation mechanism can effectively meet the preferences and needs of users, maximize the cohesion between media and users, and ensure that the number of customers will not be lost in business operations. However, the news algorithm recommendation mechanism also has many problems of its own. This article analyzes it from the following multiple angles. First of all, starting from the theory of agenda setting in the field of news communication, it can be concluded that agenda setting under the news algorithm recommendation mechanism is partially invalid, but the mainstream media can still take on the role of replacement. Secondly, it analyzes the two aspects of information rights and privacy rights from the perspective of law and interprets the legal effect of news algorithm recommendation from two different normative paths. Finally, through sociological discussion of technology ethics, it is necessary to establish a more unified social norm to deal with various ethical crises that technology may bring. [ABSTRACT FROM AUTHOR]