Evaluating new media strategy performance through big data and intelligent algorithms - Guo, Lin; Li, Charlie Quanlin (Edelweiss Applied Science and Technology, 2025)
The emergence and extensive development of new media have put pressure on traditional media and created inevitable competition between the two sides. The integration of media has come into being, which is the result of social progress. In some respects, the integration and development of media also provide new requirements and higher standards for the modernization and development of media. This kind of integration stimulates and broadens the dissemination path of media information and leads the continuous development of social information resources towards the path of sharing. At the same time, this also points out the direction for the development of traditional media and guides the development of the entire media industry towards a diversified development path. In order to more accurately evaluate the performance of new media strategies, this paper conducts a systematic study using big data technology. Specifically, the article introduces three traditional or new intelligent algorithms to conduct in-depth research on specific new media cases. By predicting 10 independent metric variables involved in the case, the prediction effect of three different algorithms is compared. The comparison results show that the multi-dimensional support vector machine optimized based on the immune algorithm has the best prediction effect. This indicates that big data technology can be effectively applied to the evaluation research of new media strategy performance. At the same time, the algorithms mentioned above can also provide a certain reference for the formulation of coping strategies in the new media era.