# Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis

arXiv: https://arxiv.org/abs/2510.05761 · HTML: https://mlsanity.com/paper/2510.05761/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 58 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Hate-CLIPper: Multimodal Hateful Meme Classification based on Cross-modal Interaction of CLIP Features](https://mlsanity.com/paper/2210.05916/) (2022)
- [Agent Instructs Large Language Models to be General Zero-Shot Reasoners](https://mlsanity.com/paper/2310.03710/) (2023)
- [A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances](https://mlsanity.com/paper/2005.11041/) (2020)
- [Virality Prediction and Community Structure in Social Networks](https://mlsanity.com/paper/1306.0158/) (2013)
- [Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes](https://mlsanity.com/paper/2101.06535/) (2021)
- [Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study](https://mlsanity.com/paper/2202.08894/) (2022)
- [Dank or Not? -- Analyzing and Predicting the Popularity of Memes on Reddit](https://mlsanity.com/paper/2011.14326/) (2020)

## Abstract (arXiv)

Memes are a central part of online culture, yet their virality remains difficult to predict, especially in cross-lingual settings. We present a large-scale, time-series dataset of 46,578 Reddit memes collected from 25 meme-centric subreddits across eight language groups, with more than one million engagement tracking points. We propose a data-driven definition of virality based on a Hybrid Score that normalises engagement by community size and integrates dynamic features such as velocity and acceleration. This approach directly addresses the field's reliance on static, simple volume-based thresholds with arbitrary cut-offs. Building on this target, we construct a multimodal feature set that combines Visual, Textual, Contextual, Network, and Temporal signals, including structured annotations from a multimodal LLM to scale cross-lingual content labelling in a consistent way. We benchmark interpretable baselines (XGBoost, MLP) against end-to-end deep models (BERT, InceptionV3, CLIP) across early observation windows from 30 to 420 minutes. Our best model, a multimodal XGBoost classifier, achieves a PR AUC of 0.43 at 30 minutes and 0.80 at 420 minutes, indicating that early prediction of meme virality is feasible even under strong class imbalance. The results reveal a clear Content Ceiling, where content-only and deep multimodal baselines plateau at low PR AUC, while structural Network and Temporal features are necessary to surpass this limit. A SHAP-based temporal analysis further uncovers an evidentiary transition, where early predictions are dominated by network priors (author and community context), and later predictions increasingly rely on temporal dynamics (velocity, acceleration) as engagement accumulates. Overall, we reframe meme virality as a dynamic, path-dependent process governed by exposure and early interaction patterns rather than by intrinsic content alone.