社交媒体中神经网络的流行标签推
Mohammad Jafari Sadr1, Seyedeh Leili Mirtaheri2, Sergio Greco3
1Department of Computer Science, Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, Iran.
Computational intelligence and neuroscience
|June 8, 2023
概括
这项研究开发了一种新的方法,通过将图像分类和GPT-2语言模型结合起来,生成流行的Instagram标签. 新的算法显著提高了病毒营销的标签相关性和趋势性.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
- 营销 营销 营销 营销 营销 营销
背景情况:
- 社交媒体的成功不仅仅依赖于视觉效果.
- 用于病毒式营销和吸引公众的特色照片至关重要.
- Instagram数据提供了对受欢迎内容的洞察力.
研究的目的:
- 确定用于病毒营销的照片特征的最佳方法.
- 开发一个算法来生成流行的和有吸引力的Instagram标签.
- 探索内容受欢迎程度在社会科学和营销中的双重应用.
主要方法:
- 搜索了57万张Instagram照片和140万个标签.
- 训练了一个ResNet模型用于多标签图像分类.
- 开发了一个GPT-2语言模型,用于基于图像特征生成流行的标签.
主要成果:
- 图像分类和GPT-2的结合方法显著提高了标签生成.
- 与基线模型相比,开发的算法产生了11%的相关性,可接受性和趋势标签.
- 识别了流行社交媒体照片的关键组件和特征.
结论:
- 这项研究提供了一种创新的方法,通过人工智能驱动的标签生成来创建病毒式营销内容.
- 这些发现对社会科学研究的消费者偏好和营销策略都有影响.
- 该研究展示了先进的人工智能模型在社交媒体优化方面的实际应用.
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