管理评论

管理评论 ›› 2024, Vol. 36 ›› Issue (11): 235-247.

• 运作管理 • 上一篇    

多源异构数据融合视角下文化UGC传播效果预测——基于GRA-PSO-WRF的组合建模

倪渊1,2, 李晓娜1, 张健1,2, 房津玉1, 李思远1   

  1. 1. 北京信息科技大学经济管理学院, 北京 100192;
    2. 绿色发展大数据决策北京市重点实验室, 北京 100192
  • 收稿日期:2022-09-20 发布日期:2024-12-09
  • 作者简介:倪渊(通讯作者),北京信息科技大学经济管理学院教授,博士生导师,博士;李晓娜,北京信息科技大学经济管理学院硕士研究生;张健,北京信息科技大学经济管理学院教授,博士生导师,博士;房津玉,北京信息科技大学经济管理学院硕士研究生;李思远,北京信息科技大学经济管理学院硕士研究生。
  • 基金资助:
    国家重点研发计划青年科学家项目（2021YFF0900200）。

Prediction of Cultural UGC Communication Effectiveness from the Perspective of Multi-source Heterogeneous Data Fusion: A Combination Modeling of GRA-PSO-WRF Method

Ni Yuan1,2, Li Xiaona1, Zhang Jian1,2, Fang Jinyu1, Li Siyuan1   

  1. 1. School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192;
    2. Beijing Key Laboratory of Green Development Big Data Decision, Beijing 100192
  • Received:2022-09-20 Published:2024-12-09

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摘要： 有效预测短视频平台环境下文化UGC的传播效果，不仅有利于提升平台对海量视频的运营效率，实现精准推荐服务，对内容资源交易以及高质量UGC创作也具有重要意义。本文根据拉斯韦尔“5W”传播理论，构建文化UGC传播效果预测指标体系，融合运营统计数据、第三方统计数据以及用户评论数据等多源异构数据，提出了一种GRA-PSO-WRF的组合预测模型。模型采用综合灰色关联分析提取融合数据特征，通过粒子群优化加权随机森林算法实现UGC传播效果预测，基于90部文化UGC在四个短视频平台的传播数据进行实证分析。结果表明：基于拉斯韦尔“5W”理论构建的文化UGC传播效果预测指标体系能够系统刻画文化内容资源传播过程的关键因素，具有良好预测适用性；多源异构数据融合的GRA-PSO-WRF模型预测精度明显优于单一RF模型、BP模型，且具备一定的可解释性；不同特征对传播效果影响呈现高、中、低三个层次，传播内容因素与传播渠道因素的最优权重相对更高，是影响文化UGC传播效果的关键所在。本文结论有效补充了组合预测、多源异构数据融合建模、可解释性机器学习的相关技术，对UGC创作及平台内容运营具有一定实践启示。

关键词: 文化UGC, 传播效果预测, 综合灰色关联分析, 粒子群优化算法, 加权随机森林

Abstract: Forecasting the dissemination effect of cultural UGC in the short video platform environment is not only helpful to improve the operation efficiency of the platform for massive videos and achieve accurate recommendation service, but also important for content resource trading and high-quality UGC creation. Based on Laswell’s “5W” communication theory, we construct a cultural UGC communication effect prediction index system, and propose a combined GRA-PSO-WRF prediction model by integrating multiple sources of heterogeneous data such as operation statistics, third-party statistics and user comment data. The model uses comprehensive gray correlation analysis to extract the features of the fused data, achieves the prediction of UGC dissemination effect by particle swarm optimization weighted random forest algorithm, and finally conducts an empirical analysis based on the dissemination data of 90 cultural UGCs in four short video platforms. The study results show that the prediction index system of cultural UGC based on Laswell’s “5W” theory can systematically portray the key factors in the process of cultural content resource dissemination, and has good predictive applicability. The prediction accuracy of GRA-PSO-WRF model with multi-source heterogeneous data integration is significantly better than that of single RF model and BP model, and has certain interpretability. The influence of different features on the UGC spreading effect presents three levels: high-level, medium-level and low-level, and the optimal weights of the spread content factor and the spread channel factor are relatively higher, which is the key to influencing the spread effect of cultural UGC. The conclusion of this paper effectively complements the relevant techniques of combined prediction, multi-source heterogeneous data fusion modeling, and interpretable machine learning, and has practical insights for UGC creation and platform content operation.

Key words: cultural UGC, communication effectiveness prediction, comprehensive gray correlation analysis, particle swarm optimization, weighted random forest