Predicting Post Virality with Temporal Cross-Attention over Trend Signals

TL;DR

This paper introduces ViralityNet, a model that enhances social media virality prediction by integrating temporal trend signals from Wikipedia with internal platform features, outperforming text-only methods.

Contribution

The paper presents a novel architecture that fuses internal post features with external temporal signals using cross-attention, improving virality prediction accuracy.

Findings

Incorporating Wikipedia trend signals improves AUC-PR by +0.015 over text-only models.

ViralityNet achieves an overall AUC-ROC of 0.836 in predicting Reddit post virality.

External attention signals provide measurable benefits over static features.

Abstract

Current models for predicting social media virality rely heavily on static textual and structural features, effectively ignoring the highly dynamic nature of trend signals. We study whether real-world attention signals can improve the prediction of social-media virality beyond what post text alone reveals. We introduce ViralityNet, an architecture that predicts Reddit post virality by fusing internal platform representations with exogenous temporal signals derived from Wikipedia pageview spikes. We frame virality as a binary classification task that accounts for differences in subreddit scale, labeling posts as viral if they exceed the 90th percentile of per-subreddit engagement and a minimum absolute score threshold. ViralityNet combines four post-level streams: title embeddings, body embeddings, structural metadata, and learned subreddit embeddings with a cross-attention block that queries a daily sliding-window trends matrix encoding the top-512 Wikipedia spike terms from the preceding seven days. Empirical results suggest that incorporating external attention signals yields consistent gains, outperforming text-only baselines by +0.015 AUC-PR and achieving an overall AUC-ROC of 0.836. Overall, we provide evidence that incorporating external attention signals yields measurable improvements over text-only baselines, highlighting the importance of real-world dynamics in shaping online virality.