Analysis of news communication strategies in the era of full media based on data mining

Abstract

Abstract In this paper, we first study the representation of news text, build the content tree using the CTWE text method, and combine the word vector with the interrelationship between words in the content tree. Then, a feature word extraction technique is applied to filter key information, and Single-pass hierarchical clustering algorithm is used to classify the news content, and a data mining algorithm is applied to realize news delivery. Finally, the impact of information’s initial value and credibility on the transmission process is examined, and the model’s feasibility and practicality are assessed. The results show that the RMSE value of the data mining model is 0.0408, the Pearson correlation coefficient is 0.9334, and the cosine similarity is 0.9596, and the model in this paper has the smallest deviation from the real data and the greatest similarity compared with other models. This study confirms the unique advantages of the data mining model in news dissemination.