# Contrastive Learning for Implicit Social Factors in Social Media Popularity Prediction

arXiv: https://arxiv.org/abs/2410.09345 · HTML: https://mlsanity.com/paper/2410.09345/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Builds on (most cited first, 16 of 16)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [Representation Degeneration Problem in Training Natural Language Generation Models](https://mlsanity.com/paper/1907.12009/) (2019)
- [CLEAR: Contrastive Learning for Sentence Representation](https://mlsanity.com/paper/2012.15466/) (2020)
- [Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation](https://mlsanity.com/paper/2110.05730/) (2021)
- [Rank-N-Contrast: Learning Continuous Representations for Regression](https://mlsanity.com/paper/2210.01189/) (2022)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction](https://mlsanity.com/paper/2311.04635/) (2023)
- [Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks](https://mlsanity.com/paper/1712.04443/) (2017)
- [XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2104.10907/) (2021)
- [SMP Challenge: An Overview of Social Media Prediction Challenge 2019](https://mlsanity.com/paper/1910.01795/) (2019)
- [Improving Social Media Popularity Prediction with Multiple Post Dependencies](https://mlsanity.com/paper/2307.15413/) (2023)

## Built on by (most cited first, 2 of 2)

- [OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction](https://mlsanity.com/paper/2604.26252/) (2026)
- [HyperFusion: Hierarchical Multimodal Ensemble Learning for Social Media Popularity Prediction](https://mlsanity.com/paper/2507.00926/) (2025)

## Abstract (arXiv)

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.