# Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction

arXiv: https://arxiv.org/abs/2606.27539 · HTML: https://mlsanity.com/paper/2606.27539/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 77 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 29 of 29)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [OASIS: Open Agent Social Interaction Simulations with One Million Agents](https://mlsanity.com/paper/2411.11581/) (2024)
- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity](https://mlsanity.com/paper/1506.02594/) (2015)
- [The spread of low-credibility content by social bots](https://mlsanity.com/paper/1707.07592/) (2017)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads](https://mlsanity.com/paper/1511.07487/) (2015)
- [DeepCas: an End-to-end Predictor of Information Cascades](https://mlsanity.com/paper/1611.05373/) (2016)
- [Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps](https://mlsanity.com/paper/2204.11073/) (2022)
- [Anyone Can Become a Troll: Causes of Trolling Behavior in Online Discussions](https://mlsanity.com/paper/1702.01119/) (2017)
- [A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances](https://mlsanity.com/paper/2005.11041/) (2020)
- [Conversation Modeling on Reddit using a Graph-Structured LSTM](https://mlsanity.com/paper/1704.02080/) (2017)
- ["I'm in the Bluesky Tonight": Insights from a Year Worth of Social Data](https://mlsanity.com/paper/2404.18984/) (2024)
- [CasSeqGCN: Combining Network Structure and Temporal Sequence to Predict Information Cascades](https://mlsanity.com/paper/2110.06836/) (2021)
- [Predicting the Popularity of Micro-videos with Multimodal Variational Encoder-Decoder Framework](https://mlsanity.com/paper/2003.12724/) (2020)
- [A Multimodal Approach to Predict Social Media Popularity](https://mlsanity.com/paper/1807.05959/) (2018)
- [How to Become Instagram Famous: Post Popularity Prediction with Dual-Attention](https://mlsanity.com/paper/1809.09314/) (2018)
- [PopSim: Social Network Simulation for Social Media Popularity Prediction](https://mlsanity.com/paper/2512.02533/) (2025)
- [SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge](https://mlsanity.com/paper/2405.10497/) (2024)
- [SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity](https://mlsanity.com/paper/2503.04446/) (2025)
- [Forecasting the Buzz: Enriching Hashtag Popularity Prediction with LLM Reasoning](https://mlsanity.com/paper/2510.08481/) (2025)
- [A Comparison of Methods for Cascade Prediction](https://mlsanity.com/paper/1606.05730/) (2016)
- [Modelling structure and predicting dynamics of discussion threads in online boards](https://mlsanity.com/paper/1801.10082/) (2018)
- [Conspiracy vs science: A large-scale analysis of online discussion cascades](https://mlsanity.com/paper/2006.00765/) (2020)
- [AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Models](https://mlsanity.com/paper/2502.18040/) (2025)

## Abstract (arXiv)

Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations. Accurate prediction enables key downstream applications, such as advertising optimization and strategic content planning by users, creators, and platforms. Despite substantial progress, existing popularity prediction works often fail to jointly consider multimodal content and temporal social interaction signals. Moreover, the literature remains highly fragmented across datasets, modalities, observation windows, prediction targets, and evaluation protocols. This fragmentation prevents fair comparison and obscures a systematic understanding of how textual, visual, temporal, and interaction-based signals jointly shape popularity dynamics. To address these challenges, we introduce MMG-Pop, a Multi-modal Graph-based Popularity Prediction benchmark, which unifies datasets, modalities, temporal interaction signals, and representative baselines under a standardized evaluation protocol. Furthermore, we propose MMG-PopNet, a unified multi-modal graph-based network that jointly models the aforementioned multi-modal signals and graph-structured social interactions. Extensive experiments on MMG-Pop, comprising four datasets across Bluesky and Reddit platforms, demonstrate the superior performance of MMG-PopNet and yield new insights into cross-platform training generalization, multi-task prediction benefits, multi-modality contributions, and LLM prediction limitation. These findings establish a unified foundation for future research on social dynamics modeling and intervention under heterogeneous modalities and socially-aware agentic ecosystem paradigms.