arXiv:2407.00556v1 Announce Type: new
Abstract: 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.
The Applicability of CLIP-based Features in Social Media Popularity (SMP) Prediction
Social media popularity (SMP) prediction is a complex task that requires integration of multi-modal data. In recent years, pre-trained vision-language models (VLMs) like CLIP have gained popularity and have been widely adopted for this task. However, the effectiveness of these models in capturing the unique characteristics of social media content has been largely unexplored.
This paper critically examines the applicability of CLIP-based features in SMP prediction, with a particular focus on the phenomenon of semantic inconsistency between images and text in social media posts. It has been observed that as post popularity increases, the semantic inconsistency also increases, thereby challenging the conventional use of VLM features.
The significance of this research lies in its comprehensive investigation of semantic inconsistency across different popularity intervals. By analyzing the impact of VLM feature adaptation on SMP tasks, the researchers uncover crucial insights for developing more targeted approaches in social media analysis.
The findings of this study demonstrate that incorporating measures of inconsistency and adapted text features significantly improve the performance of SMP prediction models. The proposed model achieves a Spearman’s Rank Correlation (SRC) of 0.729 and a Mean Absolute Error (MAE) of 1.227.
The Multi-disciplinary Nature of the Concepts
This research has a multi-disciplinary nature that spans across several fields including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The integration of vision and language models in analyzing social media content is a key area of interest in multimedia information systems. By focusing on social media popularity prediction, which heavily relies on visual and textual information, the study contributes to advancing the field of multimedia information systems.
The incorporation of CLIP-based features and the investigation of semantic inconsistency between images and text also have implications in the field of animations. As social media platforms are increasingly used to share animated content, understanding the relationship between images and text becomes crucial for accurate popularity prediction.
Furthermore, the study indirectly relates to artificial reality, augmented reality, and virtual realities. These technologies rely on the seamless integration of visual and textual information to create immersive experiences. By uncovering the challenges posed by semantic inconsistency in social media content, the research contributes to improving the accuracy and realism of these immersive technologies.
In conclusion, this research on the applicability of CLIP-based features in social media popularity prediction provides valuable insights into understanding the unique characteristics of social media content. By incorporating measures of semantic inconsistency and adapted text features, the proposed model achieves improved performance. The study’s multi-disciplinary nature contributes to the wider fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.