Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction

On social media sharing platforms, some posts are inherently destined for popularity. Therefore, understanding the reasons behind this phenomenon and predicting popularity before post publication holds significant practical value. The previous work predominantly focuses on enhancing post content extraction for better prediction results. However, certain factors introduced by social platforms also impact post popularity, which has not been extensively studied. For instance, users are more likely to engage with posts from individuals they follow, potentially influencing the popularity of these posts. We term these factors, unrelated to the explicit attractiveness of content, as implicit social factors. Through the analysis of users' post browsing behavior (also validated in public datasets), we propose three implicit social factors related to popularity, including content relevance, user influence similarity, and user identity. To model the proposed social factors, we introduce three supervised contrastive learning tasks. For different task objectives and data types, we assign them to different encoders and control their gradient flows to achieve joint optimization. We also design corresponding sampling and augmentation algorithms to improve the effectiveness of contrastive learning. Extensive experiments on the Social Media Popularity Dataset validate the superiority of our proposed method and also confirm the important role of implicit social factors in popularity prediction. We open source the code at https://github.com/Daisy-zzz/PPCL.git.

Similarity Guided Multimodal Fusion Transformer for Semantic Location Prediction in Social Media

The purpose of semantic location prediction is to extract relevant semantic location information from multimodal social media posts, offering a more contextual understanding of daily activities compared to GPS coordinates. However, this task becomes challenging due to the presence of noise and irrelevant information in "text-image" pairs. Existing methods suffer from insufficient feature representations and fail to consider the comprehensive integration of similarity at different granularities, making it difficult to filter out noise and irrelevant information. To address these challenges, we propose a Similarity-Guided Multimodal Fusion Transformer (SG-MFT) for predicting social users' semantic locations. First, we utilize a pre-trained large-scale vision-language model to extract high-quality feature representations from social media posts. Then, we introduce a Similarity-Guided Interaction Module (SIM) to alleviate modality heterogeneity and noise interference by incorporating coarse-grained and fine-grained similarity guidance for modality interactions. Specifically, we propose a novel similarity-aware feature interpolation attention mechanism at the coarse level, leveraging modality-wise similarity to mitigate heterogeneity and reduce noise within each modality. Meanwhile, we employ a similarity-aware feed-forward block at the fine level, utilizing element-wise similarity to further mitigate the impact of modality heterogeneity. Building upon pre-processed features with minimal noise and modal interference, we propose a Similarity-aware Feature Fusion Module (SFM) to fuse two modalities with cross-attention mechanism. Comprehensive experimental results demonstrate the superior performance of our proposed method in handling modality imbalance while maintaining efficient fusion effectiveness.

HierCas: Hierarchical Temporal Graph Attention Networks for Popularity Prediction in Information Cascades

Information cascade popularity prediction is critical for many applications, including but not limited to identifying fake news and accurate recommendations. Traditional feature-based methods heavily rely on handcrafted features, which are domain-specific and lack generalizability to new domains. To address this problem, researchers have turned to neural network-based approaches. However, existing methods follow a sampling-based modeling approach, potentially losing continuous dynamic information and structural-temporal dependencies that emerge during the information diffusion process. In this paper, we propose a novel framework called Hierarchical Temporal Graph Attention Networks for cascade popularity prediction (HierCas). Unlike existing methods, HierCas operates on the entire cascade graph by a dynamic graph modeling approach, enabling it to capture the full range of continuous dynamic information and explicitly model the interplay between structural and temporal factors. By leveraging time-aware node embedding, graph attention mechanisms and hierarchical pooling structures, HierCas effectively captures the popularity trend implicit in the complex cascade. Extensive experiments conducted on two real-world datasets in different scenarios demonstrate that our HierCas significantly outperforms the state-of-the-art approaches.

View while Moving: Efficient Video Recognition in Long-untrimmed Videos

Recent adaptive methods for efficient video recognition mostly follow the two-stage paradigm of "preview-then-recognition" and have achieved great success on multiple video benchmarks. However, this two-stage paradigm involves two visits of raw frames from coarse-grained to fine-grained during inference (cannot be parallelized), and the captured spatiotemporal features cannot be reused in the second stage (due to varying granularity), being not friendly to efficiency and computation optimization. To this end, inspired by human cognition, we propose a novel recognition paradigm of "View while Moving" for efficient long-untrimmed video recognition. In contrast to the two-stage paradigm, our paradigm only needs to access the raw frame once. The two phases of coarse-grained sampling and fine-grained recognition are combined into unified spatiotemporal modeling, showing great performance. Moreover, we investigate the properties of semantic units in video and propose a hierarchical mechanism to efficiently capture and reason about the unit-level and video-level temporal semantics in long-untrimmed videos respectively. Extensive experiments on both long-untrimmed and short-trimmed videos demonstrate that our approach outperforms state-of-the-art methods in terms of accuracy as well as efficiency, yielding new efficiency and accuracy trade-offs for video spatiotemporal modeling.

Improving Social Media Popularity Prediction with Multiple Post Dependencies

Social Media Popularity Prediction has drawn a lot of attention because of its profound impact on many different applications, such as recommendation systems and multimedia advertising. Despite recent efforts to leverage the content of social media posts to improve prediction accuracy, many existing models fail to fully exploit the multiple dependencies between posts, which are important to comprehensively extract content information from posts. To tackle this problem, we propose a novel prediction framework named Dependency-aware Sequence Network (DSN) that exploits both intra- and inter-post dependencies. For intra-post dependency, DSN adopts a multimodal feature extractor with an efficient fine-tuning strategy to obtain task-specific representations from images and textual information of posts. For inter-post dependency, DSN uses a hierarchical information propagation method to learn category representations that could better describe the difference between posts. DSN also exploits recurrent networks with a series of gating layers for more flexible local temporal processing abilities and multi-head attention for long-term dependencies. The experimental results on the Social Media Popularity Dataset demonstrate the superiority of our method compared to existing state-of-the-art models.