  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.
