  Social media popularity (SMP) prediction is a complex task involving
multi-modal data integration. While pre-trained vision-language models (VLMs)
like CLIP have been widely adopted for this task, their effectiveness in
capturing the unique characteristics of social media content remains
unexplored. This paper critically examines the applicability of CLIP-based
features in SMP prediction, focusing on the overlooked phenomenon of semantic
inconsistency between images and text in social media posts. Through extensive
analysis, we demonstrate that this inconsistency increases with post
popularity, challenging the conventional use of VLM features. We provide a
comprehensive investigation of semantic inconsistency across different
popularity intervals and analyze the impact of VLM feature adaptation on SMP
tasks. Our experiments reveal that incorporating inconsistency measures and
adapted text features significantly improves model performance, achieving an
SRC of 0.729 and an MAE of 1.227. These findings not only enhance SMP
prediction accuracy but also provide crucial insights for developing more
targeted approaches in social media analysis.
