基于深度媒体化和人工智能的博物馆社交媒体沟通的应用
Hongkai Wang1, Chao Song2, Hongming Li3
1School of Jewellery and Art Design, Beijing Institute of Economics and Management, Beijing, 100102, China.
Scientific reports
|November 20, 2024
概括
使用卷积神经网络 (CNN) 的人工智能 (AI) 显著提高了博物馆的社交媒体参与度. 这项技术增强了在线文化机构的内容分享,参与和覆盖范围.
科学领域:
- 数字人文学科 数字人文学科
- 计算机科学 计算机科学
- 博物馆研究 博物馆研究
背景情况:
- 博物馆越来越依赖社交媒体来吸引公众参与.
- 优化社交媒体内容的传统方法通常是手动的,耗时的.
- 深度媒体化理论为理解媒体在社会中的作用提供了一个框架.
研究的目的:
- 调查卷积神经网络 (CNN) 技术的应用,以改善博物馆的社交媒体沟通.
- 量化AI驱动的内容分析对关键社交媒体指标的影响.
主要方法:
- 从四个不同的博物馆收集了社交媒体内容.
- 构建了一个由数万张图像组成的大数据集.
- 设计并训练了一种CNN模型,用于自动识别和分类图像内容.
- 通过实验测试评估模型的性能.
主要成果:
- 美国有线电视新闻网 (CNN) 模式显著增加了用户参与率 (66.7%的增长).
- 内容覆盖率大幅增加 (75%的增长).
- 用户保留率提高了100%,内容共享率提高了200%.
结论:
- 美国有线电视新闻网 (CNN) 技术为提高博物馆社交媒体表现提供了强大而适用的解决方案.
- 人工智能驱动的分析可以有效地优化文化机构的数字通信策略.
- 该模型在不同的博物馆类型和技术配置中展示了多功能性.
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