  Pop culture is an important aspect of communication. On social media people
often post pop culture reference images that connect an event, product or other
entity to a pop culture domain. Creating these images is a creative challenge
that requires finding a conceptual connection between the users' topic and a
pop culture domain. In cognitive theory, this task is called conceptual
blending. We present a system called PopBlends that automatically suggests
conceptual blends. The system explores three approaches that involve both
traditional knowledge extraction methods and large language models. Our
annotation study shows that all three methods provide connections with similar
accuracy, but with very different characteristics. Our user study shows that
people found twice as many blend suggestions as they did without the system,
and with half the mental demand. We discuss the advantages of combining large
language models with knowledge bases for supporting divergent and convergent
thinking.
