The rapid development of the Internet has profoundly changed human life. Humans are increasingly expressing themselves and interacting with others on social media platforms. However, although artificial intelligence technology has been widely used in many aspects of life, its application in social media content creation is still blank. To solve this problem, we propose a new prompt word generation framework based on multi-modal information fusion, which combines multiple tasks including topic classification, sentiment analysis, scene recognition and keyword extraction to generate more comprehensive prompt words. Subsequently, we use a template containing a set of prompt words to guide ChatGPT to generate high-quality tweets. Furthermore, in the absence of effective and objective evaluation criteria in the field of content generation, we use the ChatGPT tool to evaluate the results generated by the algorithm, making large-scale evaluation of content generation algorithms possible. Evaluation results on extensive content generation demonstrate that our cue word generation framework generates higher quality content compared to manual methods and other cueing techniques, while topic classification, sentiment analysis, and scene recognition significantly enhance content clarity and its consistency with the image.

翻译：互联网的迅猛发展深刻改变了人类生活。人们越来越多地在社交媒体平台上表达自我并与他人互动。然而，尽管人工智能技术已在生活的诸多方面得到广泛应用，其在社交媒体内容创作领域的应用仍属空白。为解决这一问题，我们提出了一种基于多模态信息融合的新型提示词生成框架，该框架融合了主题分类、情感分析、场景识别与关键词提取等多重任务，以生成更全面的提示词。随后，我们使用包含一组提示词的模板来引导ChatGPT生成高质量的推文。此外，针对内容生成领域缺乏有效客观评估标准的问题，我们借助ChatGPT工具对算法生成结果进行评估，使得大规模评估内容生成算法成为可能。在广泛的内容生成评估中，结果表明相较于人工方法及其他提示技术，我们的提示词生成框架能够产生更高质量的内容，同时主题分类、情感分析与场景识别显著提升了内容的清晰度及其与图像的契合度。