Large Language Models Are Natural Video Popularity Predictors (2025)

Abstract: Predicting video popularity is often framed as a supervised learning task, relying heavily on meta-information and aggregated engagement data. However, video popularity is shaped by complex cultural and social factors that such approaches often overlook. We argue that Large Language Models (LLMs), with their deep contextual awareness, can better capture these nuances. To bridge the gap between pixel-based video data and token-based LLMs, we convert frame-level visuals into sequential text representations using Vision-Language Models. This enables LLMs to process multimodal content—titles, frame-based descriptions, and captions—capturing both engagement intensity (view count) and geographic spread (number of countries where a video trends). On 13,639 popular videos, a supervised neural network using content embeddings achieves 80% accuracy, while our LLM-based approach reaches 82% without fine-tuning. Combining the neural network’s predictions with the LLM further improves accuracy to 85.5%. Moreover, the LLM generates interpretable, attribute-based explanations for its predictions. Manual validations confirm the quality of these hypotheses and address concerns about hallucinations in the video-to-text conversion process. Overall, our findings suggest that LLMs, equipped with text-based multimodal representations, offer a powerful, interpretable, and data-efficient solution for tasks requiring rich contextual insight, such as video popularity prediction.

## introduction video has become the dominant medium for online content consumption, accounting for the majority of internet traffic and continuing to grow rapidly @xcite . this makes video popularity prediction a critical task for content creators, social media platforms, and advertisers. beyond its commercial relevance, predicting which videos will * these authors jointly supervised this work. despite the growing interest in this area, most research has used statistical or supervised learning approaches centered on meta-information and aggregated engagement metrics (e.g., uploader reputation, view/comment/like counts, external social network size) @xcite @xcite . while these signals are valuable, they generally reflect early user reactions rather than the deeper contextual and cultural nuances of the video content. these intrinsic qualities of video content may play a critical role in how a video resonates with both local and global audiences. however, traditional approaches struggle to process and leverage such rich information due to limited capacity in processing complex multimodal data. in this paper, we propose a novel approach to video popularity prediction that shifts the focus to the intrinsic qualities of a video's textual, verbal, and visual content, excluding after-the-fact user engagement data such as early view counts and social network signals. our method leverages the power of vision-language models (vlms) and large language models (llms) to extract and interpret these intrinsic qualities, complemented by conventional descriptors such as titles and descriptions. more specifically, to address the challenge of integrating pixel-based video data with the token-based architecture of llms, we use vlms to transform frame-level visual data into sequential textual representations. these representations are then combined with conventional video descriptors such as titles, descriptions, and captions (extracted from the video's audio) to create a comprehensive multimodal textual representation. this transformation allows llms to perform the video popularity prediction task, effectively capturing both vertical (e.g., view counts) and horizontal (e.g., geographic spread) aspects through its contextual understanding of textual content. empirically, we introduce a novel prompting strategy that integrates supervised learning signals into llm-based predictions, outperforming deep learning models based on content embeddings and yielding interpretable, attribute-based explanations. additionally, we present the global popular video dataset (gpvd), a large-scale dataset of 1.1m unique popular youtube videos, enriched with titles, descriptions, and detailed metadata. this dataset uniquely includes two key popularity metrics-view counts and number of countries where a video trended-spanning 109 countries. the latter metric, which reflects the video's global reach, has been overlooked in prior research but is crucial for a comprehensive understanding of video virality. our experiments use a balanced subset covering different popularity classes, varying engagement intensity and global reach, as detailed in the methods section. furthermore, we address concerns related to hallucinations in video-to-text conversion and validate the quality of attribute-based explanations through survey experiments. the key contributions of this paper are as follows: ## related work video popularity prediction video popularity prediction has traditionally been approached as a supervised learning task, often aimed at estimating view counts using features such as title length, runtime, and early user engagement metrics (e.g., comments and likes) @xcite @xcite . later work introduced time-series analysis and user behavior modeling to track changes in popularity over time @xcite @xcite @xcite . however, these methods mostly rely on aggregated platform metrics and struggle to capture the cultural and social dynamics that can significantly influence a video's success. further, the majority of these studies treat popularity as a single-dimensional concept, typically focusing on view counts. yet a video may accumulate high views in a limited region without achieving broader international reach. for instance, as @xcite illustrate that region-specific cultural preferences can inflate view counts locally without translating into global appeal. to address this gap, we expand the notion of popularity to include geographic spread (global vs. local reach) alongside engagement intensity (view counts). we argue that large language models (llms), equipped with extensive contextual knowledge, are uniquely poised to capture these subtleties in a way that earlier approaches-tied to simpler, often unimodal features-could not. ## methods our method predicts video popularity by converting key frames into textual descriptions and integrating them with video metadata for llm-based inference. figure 1 illustrates two main stages, following the task definition and dataset description: ## experiments and results implementation details we conducted experiments using claude sonnet 3.5 @xcite for reasoning tasks and llama 3 @xcite for zero-shot and in-context learning. additional implementation details, including specific configurations for llama, are provided in appendix a.3. the supervised baseline model was trained with the adam optimizer (learning @xmath0.001) and early stopping (@xmath1. additional details are included in appendix a.8. ## discussion and future work the results of our experiments demonstrate the effectiveness of our approach to video popularity prediction, where llms are progressively enhanced through structured prompting techniques. in particular, the performance improvements-from the vanilla prompt to the final model incorporating hypothesis generation and supervised signalsunderscore the potential of combining llm reasoning capabilities with supervised learning methods. notably, hypothesis generation not only improved performance as a major contributor to the gains but also enhanced explainability-validated through survey experiments-making predictions more transparent and providing insights into factors driving video popularity. our extensive ablation studies and robustness checks (appendix a.6 to a.10) demonstrate that the framework maintains consistent performance across temperature settings, model architectures, and video languages, underscoring its generalizability and reliability under varying conditions. overall, the results indicate that our approach provides a highly effective solution for multimodal prediction tasks, surpassing traditional supervised models and simpler methods that rely on examplebased guidance, such as few-shot and near examples. additionally, by incorporating two key dimensions of popularity-engagement intensity and geographic spread-our framework delivers a more nuanced and comprehensive understanding of the factors driving video success, moving beyond the one-dimensional view count focus prevalent in previous research. beyond video popularity prediction, the techniques developed in this study have broader implications across various domains. our vlm-to-llm pipeline and hypothesis generation methods could generalize to tasks such as trend or engagement prediction on multimodal (social media) platforms. in healthcare, the interpretability of llm-generated hypotheses may enhance transparency in medical imaging and diagnosis, where explainable ai is critical for trust and adoption. similarly, the approach could support education by offering interpretable feedback for student assessments or personalized content. these applications highlight the versatility and potential impact of our framework. future research could explore several promising directions. refining the hypothesis generation process, particularly by incorporating advanced reinforcement learning techniques, holds potential for further accuracy improvements. specifically, the llm can act as an agent generating hypotheses about video popularity factors, with each hypothesis representing an action within the state space of possible predictions. prediction accuracy serves as a reward signal, guiding the system to learn which hypotheses are most effective. additionally, improving the model's ability to account for cultural and emotional factors could enhance predictions for content like culturally niche or emotionally resonant videos, where traditional metrics may be insufficient. a deeper understanding of these factors could also enable the model to better align with user contexts, gauging video appeal based on situational factors and intent. further integration of multimodal data, such as audio features, could offer richer insights into the drivers of video popularity. finally, future work could explore real-time prediction capabilities and adapt this framework to predict trends across platforms beyond youtube. ## limitations interpreting llm reasoning our approach is designed to capture both geographic spread and engagement intensity. while the model's generated hypotheses often reference attributes from both dimensions as reported in section 4.3.2 (e.g., content featuring prominent teams or novel gaming challenges; see appendix a.11 for more detailed examples), it remains unclear whether the llm consistently relies on these specific factors throughout its reasoning process. it is also important to recognize that hypothesis generation may reflect rationalizations of the llm's internal processes rather than empirically grounded explanations. although these rationales are expressed using interpretable attributes and offer useful insights, they should not be interpreted as scientific hypotheses. future work should examine whether these explanations truly reflect the model's internal decision-making-potentially through mechanistic analysis or other interpretability techniques. while hypothesis generation adds some transparency and utility, a deeper investigation is needed to validate the fidelity of these explanations. ## ethical considerations this study was deemed exempt by the irb at new york university abu dhabi, as the classification tasks did not involve human subjects and no personally identifiable information was collected during the manual validation process. crowdworkers were recruited via amazon mechanical turk and compensated at an average rate of $15/hour for tasks requiring approximately 10-15 minutes. participants were provided with a clear description of the study's purpose before opting in and were free to withdraw at any time. the dataset used in this study consists of publicly available data collected via the youtube api and excludes any personally identifiable information. while the dataset primarily features popular videos, reducing the likelihood of harmful or sensitive content, we implemented additional safeguards to further mitigate potential risks. specifically, during the manual evaluation process, we randomly sampled 100 videos to screen for potentially problematic content, such as toxicity or inappropriate material, and found no such instances in the sampled data. this thorough screening ensured that annotators were not exposed to harmful content and confirmed that the dataset does not contain severely harmful material, as anticipated.