# OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction

arXiv: https://arxiv.org/abs/2604.26252 · HTML: https://mlsanity.com/paper/2604.26252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 74 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 30 of 38)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)
- [Transformer Tracking with Cyclic Shifting Window Attention](https://mlsanity.com/paper/2205.03806/) (2022)
- [Compact Transformer Tracker with Correlative Masked Modeling](https://mlsanity.com/paper/2301.10938/) (2023)
- [A Content-Driven Micro-Video Recommendation Dataset at Scale](https://mlsanity.com/paper/2309.15379/) (2023)
- [ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval](https://mlsanity.com/paper/2604.17898/) (2026)
- [OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval](https://mlsanity.com/paper/2507.05631/) (2025)
- [Semantic-Aware Logical Reasoning via a Semiotic Framework](https://mlsanity.com/paper/2509.24765/) (2025)
- [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014)
- [STABLE: Efficient Hybrid Nearest Neighbor Search via Magnitude-Uniformity and Cardinality-Robustness](https://mlsanity.com/paper/2604.01617/) (2026)
- [INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval](https://mlsanity.com/paper/2604.18051/) (2026)
- [SF2T: Self-supervised Fragment Finetuning of Video-LLMs for Fine-Grained Understanding](https://mlsanity.com/paper/2504.07745/) (2025)
- [Logical Phase Transitions: Understanding Collapse in LLM Logical Reasoning](https://mlsanity.com/paper/2601.02902/) (2026)
- [GA-S3: Comprehensive Social Network Simulation with Group Agents](https://mlsanity.com/paper/2506.03532/) (2025)
- [HINT: Composed Image Retrieval with Dual-path Compositional Contextualized Network](https://mlsanity.com/paper/2603.26341/) (2026)
- [Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval](https://mlsanity.com/paper/2604.19386/) (2026)
- [Contextual LSTM (CLSTM) models for Large scale NLP tasks](https://mlsanity.com/paper/1602.06291/) (2016)
- [Coupling Macro Dynamics and Micro States for Long-Horizon Social Simulation](https://mlsanity.com/paper/2604.05516/) (2026)
- [TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval](https://mlsanity.com/paper/2604.21806/) (2026)
- [MVP: Winning Solution to SMP Challenge 2025 Video Track](https://mlsanity.com/paper/2507.00950/) (2025)
- [Understanding LLM Reasoning for Abstractive Summarization](https://mlsanity.com/paper/2512.03503/) (2025)
- [IntervenSim: Intervention-Aware Social Network Simulation for Opinion Dynamics](https://mlsanity.com/paper/2604.06600/) (2026)
- [Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework](https://mlsanity.com/paper/2003.12724/) (2020)
- [Predicting popularity of online videos using Support Vector Regression](https://mlsanity.com/paper/1510.06223/) (2015)
- [A Multimodal Approach to Predict Social Media Popularity](https://mlsanity.com/paper/1807.05959/) (2018)
- [Modeling and Counteracting Exposure Bias in Recommender Systems](https://mlsanity.com/paper/2001.04832/) (2020)
- [StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs](https://mlsanity.com/paper/2505.22950/) (2025)
- [Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media](https://mlsanity.com/paper/2105.08809/) (2021)
- [PopSim: Social Network Simulation for Social Media Popularity Prediction](https://mlsanity.com/paper/2512.02533/) (2025)
- [Forecasting Popularity of Videos using Social Media](https://mlsanity.com/paper/1403.5603/) (2014)

## Built on by (most cited first, 1 of 1)

- [Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse](https://mlsanity.com/paper/2608.10810/) (2026)

## Abstract (arXiv)

Predicting social media popularity requires understanding both the intrinsic appeal of content and the external context that determines how it is exposed to users. Existing methods focus on content signals but do not separate them from exposure-related patterns, which causes the learned representations to absorb platform-specific visibility effects and weakens both interpretability and cross-platform transfer. This paper introduces OmniTrend, a unified framework that models popularity as the joint outcome of content attractiveness and contextual exposure. The content module learns cross-modal representations from visual, audio, and textual cues to quantify intrinsic appeal, while the context module estimates exposure from exogenous signals such as posting time, author activity, topical trends, and retrieval-based neighborhood statistics. OmniTrend learns separate predictors for content attractiveness and contextual exposure and integrates them in the final popularity estimate, which makes the role of each factor explicit and supports robust transfer across image and video platforms.