Post: Jul 04,2024
cs.MM
Unmasking the Enigma of Social Media Popularity: Exploring Semantic Inconsistencies in Vision-Language Integration
Abstract: The prediction of social media popularity (SMP) poses a significant challenge due to the integration of diverse modalities. While pre-trained vision-language models (VLMs) such as CLIP have gained substantial popularity in SMP prediction, critical questions remain unanswered regarding their ability to capture the essence of social media content. In this article, we embark on a novel investigation, focusing on the often-overlooked phenomenon of semantic inconsistency between textual and visual elements in social media posts. Our aim is to shed light on the limitations of VLM-based features and provide insights to enhance SMP prediction accuracy.
Introduction: The extraordinary growth of social media platforms has paved the way for novel research opportunities, including SMP prediction. However, existing approaches predominantly rely on VLM-based features without thoroughly considering the inherent semantic inconsistencies inevitably present in social media content. These inconsistencies arise due to the diverse nature of textual descriptions and the visual context depicted in posts.
Semantic Inconsistency Analysis: To delve deeper into the challenges posed by semantic inconsistencies, we conducted an extensive analysis across a wide range of social media posts. Our findings reveal a pervasive pattern: as post popularity increases, the degree of semantic inconsistency between text and visuals becomes more pronounced. This finding raises concerns regarding the reliability and efficacy of VLM-based features for SMP prediction.
Impact of VLM Feature Adaptation: In light of the aforementioned challenges, we scrutinize the impact of VLM feature adaptation on SMP tasks. By incorporating measures of semantic inconsistency and adaptively adjusting the textual features, we aim to surmount the limitations previously overlooked in SMP prediction models. Our experiments demonstrate that these adaptive measures yield promising results, with significant improvements in model performance. Specifically, we achieve a substantial Spearman’s Rank Correlation (SRC) of 0.729 and a Mean Absolute Error (MAE) of 1.227.
Implications and Future Directions: The implications of our research extend beyond enhancing SMP prediction accuracy. Our analysis offers valuable insights into the intricacies of social media content, bridging the existing gap between textual and visual elements. By acknowledging and addressing the role of semantic inconsistencies, we pave the way for the development of more targeted approaches in social media analysis. Future research avenues include leveraging these insights to design new hybrid models that effectively integrate textual and visual features, thereby unravelling the enigma of social media popularity.
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