---
title: Short-Video Propagation Influence Rating
url: https://www.emergentmind.com/topics/short-video-propagation-influence-rating-spir
type: topic
---

# Short-Video Propagation Influence Rating

Short-video Propagation Influence Rating (SPIR) addresses the problem of forecasting the long-term, multidimensional influence of short-video content across multiple online platforms. Formally, given a newly posted short-video within a large heterogeneous propagation network, SPIR seeks to predict its future influence as an integer rating from 0 to 9, integrating cross-platform views, likes, shares, collects, comments, and followership statistics over a multi-week time frame. This task transcends single-scalar popularity forecasting, instead requiring models to reason over rich, highly structured, and heterogeneous data. The SPIR benchmark is instantiated with the XS-Video dataset, which provides a large cross-platform propagation graph, and is addressed with NetGPT, a large graph model composed of a heterogeneous GNN backbone integrated with LLM-based reasoning.

## 1. SPIR: Formal Task Definition

Let $G=(V,E,S)$ denote the propagation graph, where $V$ is the set of nodes—including videos, platforms, topics, comments, interaction statistics, and other metadata—$E$ is the set of directed, heterogeneous edges encoding multimodal relations, and $S \subseteq V$ is the set of video nodes of interest. For each video node $v \in S$, the SPIR task requires a predictor $\hat{y}_v \approx y_v$, where $y_v \in \{0,1,\ldots,9\}$ is the true, multi-dimensional influence score of the video as aggregated across five platforms in the two weeks post-publication.

Unlike traditional popularity prediction tasks that forecast a scalar indicator (e.g., number of views or likes) within a short window, SPIR fuses the relative and absolute magnitudes of views, likes, shares, collects, comments, and follower count, across heterogeneous platforms with varying statistical profiles, to create a discrete, ten-level influence rating.

## 2. XS-Video Dataset: Construction and Annotation

XS-Video is the first large-scale, cross-platform short-video propagation dataset. Its composition is as follows:

| Statistic                | Value                                                          |
|--------------------------|---------------------------------------------------------------|
| Platforms                | Douyin, Kuaishou, Xigua, Toutiao, Bilibili                    |
| Distinct Short-videos    | 117,720                                                       |
| Samples                  | 381,926                                                       |
| Topics                   | 535                                                           |
| Features per Sample      | Views, likes, shares, collects, comments, fans, comment text  |
| Content Modalities       | Video (MP4), title, description, topic, times, durations      |

Data collection spanned from 2024-11-25 to 2025-01-02; newly posted videos were collected daily for each trending topic and subsequently re-sampled every two days up to two weeks. All user identifiers were anonymized. 

Cross-platform alignment is necessary due to heterogeneity in audience size and engagement rates (e.g., Douyin’s DAU $\sim$ 0.6B vs. Xigua's MAU $\sim$ 0.2B). For each platform $*$ and set of multi-posted videos $S_*$, alignment factors $\alpha_*$ are computed by minimizing the mean squared percentage error (MSPE) between raw indicators from Kuaishou ("central platform") and those on platform $*$. Specifically:
$$
\alpha_* = \arg\min_{\alpha} \frac{1}{|S_*|} \sum_{s\in S_*} \left[ \frac{Ind_{Kuaishou}(s) - \alpha \cdot Ind_{*}(s)}{Ind_{Kuaishou}(s)+1} \right]^2
$$
Subsequently, all indicators for each video are rescaled to the Kuaishou reference, and the final two-week aggregates determine ratings via stepped thresholds: if all five indicators are zero, $y_v=0$; otherwise, the level $l \in \{1, ..., 9\}$ is the smallest index for which any aligned indicator surpasses the $l$-th threshold (e.g., for $l=3$ require views $> 10^5$, likes $>20$). This process produces a label distribution naturally peaked at level 3.

## 3. Propagation Graph and Labeling Formalism

The propagation graph $G=(V, E, S)$ has:

- $V = \{$video, platform, topic, title, description, time, ctime, video\_time, comment, likes, collects, views, shares, comments, fans$\}$,
- $E$ encoding relations: "is\_platform\_of", "is\_topic\_of", "has\_same\_author\_as", "has\_same\_topic\_as", "is\_history\_of", among others, totaling approximately $1.67 \times 10^9$ directed edges.

Ratings are formally induced by the labeling function:
$$
L(v) = Level\left(\{\alpha_* \cdot Ind_*(v): * \text{ over all platforms}\}\right)
$$
where $Level(\cdot)$ encodes the stepped-threshold discretization defined above.

## 4. NetGPT: Model Architecture and Training Regime

NetGPT integrates a heterogeneous relational graph convolutional network (RGCN) with large language models, proceeding via a three-stage training sequence.

### Stage I: Heterogeneous Graph Pretraining

- Node representation extraction depends on modality:
  - Video: $f_v^{raw} = \text{AvgPool}(\text{ViT}(v)) \in \mathbb{R}^{3584}$
  - Text (platform, topic, etc.): $f_v^{raw} = \text{RoBERTa}(v) \in \mathbb{R}^{1024}$
  - Time: sinusoidal positional encoding in $\mathbb{R}^{512}$
  - Scalar statistics: $f_v^{raw} = \log(v+1) \in \mathbb{R}^1$
  - Comments: concatenated ($\mathbb{R}^{1024}$ for text, $\mathbb{R}^{512}$ for time)
- Embeddings are updated via a two-layer RGCN:
  $$
  F' = \{ f'_v \} = \text{GNN}(F^{raw}, E), \quad f'_v \in \mathbb{R}^{d_g}
  $$
- Pretraining objective:
  $$
  \hat{y}_v = 9 \cdot \sigma(W_1 f'_v + b_1)
  $$
  $$
  L_{pt} = \frac{1}{|S_{tra}|} \sum_{v \in S_{tra}} SmoothL_1(\hat{y}_v, y_v)
  $$

### Stage II: Supervised Language Fine-tuning

- The pretrained GNN and LLM (e.g., Qwen2-VL-7B) are frozen.
- Graph embedding tokens are projected:
  $$
  e_v = W_2 f'_v + b_2, \quad e_v \in \mathbb{R}^{d_{lm}}
  $$
- The instruction prompt (tokenized, with <|graph_pad|> embedding replaced by $e_v$) is given as input:
  ```
  "You are a helpful assistant. Graph: (v,E). Please read the JSON-formatted video sample x_v, and predict its final propagation influence level (0–9)."
  ```
- Training objective:
  $$
  L_{slf} = -\sum \log P_{LLM}(\text{"The influence level is } y_v" \mid \text{ins})
  $$

### Stage III: Task-oriented Predictor Fine-tuning

- Allow last four decoder layers of the LLM and $W_2$, $b_2$ to update; new regression head is introduced.
- Final output:
  $$
  \tilde{z}_v = 9 \cdot \sigma(W_3 f_h + b_3)
  $$
  where $f_h$ is the final hidden state of the assistant's end-of-sequence token.
- Fine-tune using:
  $$
  L_{ft} = \frac{1}{|S_{tra}|} \sum SmoothL_1(\tilde{z}_v, y_v)
  $$

## 5. Evaluation Protocol and Comparative Results

The evaluation treats SPIR both as 10-class classification and as regression. Given test set $\{(y_i, \hat{y}_i) \mid i=1, ..., M\}$:

- Accuracy (ACC): $\mathbb{P}(y_i = \mathrm{round}(\hat{y}_i))$
- Mean Squared Error (MSE): $\mathbb{E}[(y_i - \hat{y}_i)^2]$
- Mean Absolute Error (MAE): $\mathbb{E}[|y_i - \hat{y}_i|]$

Table: Main Results on XS-Video Test Split (117,720 videos; train/test = 4:1)

| Model             | Input      | ACC    | MSE     | MAE     |
|-------------------|------------|--------|---------|---------|
| GCN               | Graph      |0.4474  |1.0623   |0.7599   |
| HAN               | Graph      |0.2619  |2.6666   |1.2724   |
| HetSANN           | Graph      |0.5078  |0.8917   |0.6803   |
| RGCN              | Graph      |0.6313  |0.7801   |0.5844   |
| Mistral-7B        | Text       |0.5387  |2.1000   |0.8123   |
| InternLM2.5-7B    | Text       |0.5268  |2.1110   |0.8064   |
| Llama-3.1-8B      | Text       |0.5290  |2.1215   |0.8081   |
| Qwen2.5-7B        | Text       |0.5469  |2.0820   |0.7688   |
| Llava-Next-Video  | Text+Video |0.5694  |1.8503   |0.7315   |
| Qwen2-VL-7B       | Text+Video |0.5884  |1.6820   |0.6629   |
| NetGPT            | Graph+Text |0.6777  |0.7169   |0.5457   |

NetGPT produces the highest accuracy (+7.3% relative to RGCN), lowest MSE (–8.1%), and lowest MAE (–6.6%) among graph baselines, and outperforms the strongest multimodal LLM (Qwen2-VL) by +15.2% ACC, –57.4% MSE, and –17.7% MAE.

Ablation experiments show that removing video features (NetGPT-V), disabling video–video or interactive edges (NetGPT-VV/IV), omitting comment edges (NetGPT-CV), or skipping Stage II (NetGPT-SLF) each impair performance; likewise, reducing LLM capacity results in degradation.

With less than 3 days of observation, NetGPT retains a lead over RGCN, although predictive error increases for all models. Longer observation periods reduce error as expected.

## 6. Strengths, Limitations, and Interpretative Context

Strengths:

- Comprehensive, real-world propagation dataset spanning five major Chinese short-video platforms, with rich temporal, multimodal, and relational annotation.
- Heterogeneous propagation graph comprises 5.5M nodes and 1.7B directed edges, enabling flexible large-graph processing.
- The three-stage NetGPT framework expressly integrates multimodal graph representation learning with instruction-based LLM reasoning.

Limitations:

- Considerable computational expense due to large-scale RGCN pretraining and LLM fine-tuning (8 × A800 80GB GPUs required).
- Some overfitting risk for smaller platforms (e.g., Bilibili) and potential label distribution mismatch between frequent and rare topics.
- SPIR rating granularity is fixed at 10 levels; future work could investigate continuous-valued or finer-grained influence metrics.

A plausible implication is that broader deployment of SPIR-like frameworks could face practical resource barriers, and downstream tasks may benefit from adaptive label definitions or platform-specific calibration.

## 7. Practical Applications and Future Directions

SPIR furnishes an actionable foundation for several applications:

- Commercial analytics: anticipatory identification of high-influence content to inform targeted advertising and partnership decisions.
- Public-opinion monitoring: projecting potential societal impact of emergent or policy-relevant video content.
- Recommendation systems: integrating long-term influence forecasts to improve engagement and ranking quality.
- User-behavior modeling: capturing the transfer and amplification dynamics of user interactions and content virality in heterogeneous networks.

*This suggests* further exploration of continuous influence metrics, better transferability across platforms, and incorporation into production systems for content moderation, trending detection, or influence maximization.

In sum, the SPIR framework and XS-Video dataset provide a reproducible, large-scale benchmark for forecasting multidimensional, cross-platform short-video influence. The NetGPT pipeline demonstrates the feasibility and benefits of unifying heterogeneous graph-based pretraining with LLM reasoning for long-term propagation analysis [2503.23746].

Source: https://www.emergentmind.com/topics/short-video-propagation-influence-rating-spir