早期多模态预测跨语言Reddit迷因病毒性：时间窗口分析
Early Multimodal Prediction of Cross-Lingual Meme Virality on Reddit: A Time-Window Analysis
AI总结：
本文提出了一种基于多模态特征的迷因病毒性预测方法，通过时间窗口分析展示早期预测的可行性，并揭示了网络和时间特征对突破内容限制的重要性。
摘要：
迷因是在线文化的核心组成部分，但其病毒性预测仍然困难，尤其是在跨语言环境下。我们呈现了一个大规模的时间序列数据集，包含从八个语言群体的25个以迷因为中心的子版块中收集的46,578个Reddit迷因，拥有超过一百万个参与度跟踪点。我们提出了一种基于混合分数的数据驱动病毒性定义，该分数通过社区规模标准化参与度，并整合动态特征，如速度和加速度。这种方法直接解决了该领域对静态、简单的基于体积的阈值的依赖，这些阈值具有任意的截止点。基于此目标，我们构建了一个多模态特征集，结合了视觉、文本、上下文、网络和时间信号，包括来自多模态大语言模型的结构化注释，以在一致的方式下扩展跨语言内容标注。我们将在早期观察窗口（30至420分钟）内将可解释的基线（XGBoost、MLP）与端到端深度模型（BERT、InceptionV3、CLIP）进行比较。我们的最佳模型，一个多模态XGBoost分类器，在30分钟时达到PR AUC 0.43，在420分钟时达到0.80，表明即使在强类别不平衡的情况下，早期预测迷因病毒性也是可行的。结果揭示了明显的“内容天花板”，其中仅内容和深度多模态基线在低PR AUC处趋于平缓，而结构网络和时间特征是突破这一限制所必需的。基于SHAP的时间分析进一步揭示了证据的转变，其中早期预测受网络先验（作者和社区上下文）主导，而后期预测越来越多地依赖时间动态（速度、加速度）随着参与度的积累。总体而言，我们将迷因病毒性重新定义为一个动态、路径依赖的过程，由曝光和早期互动模式而非仅由内在内容本身所决定。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
