In the age of the infodemic, it is crucial to have tools for effectively
monitoring the spread of rampant rumors that can quickly go viral, as well as
identifying vulnerable users who may be more susceptible to spreading such
misinformation. This proactive approach allows for timely preventive measures
to be taken, mitigating the negative impact of false information on society. We
propose a novel approach to predict viral rumors and vulnerable users using a
unified graph neural network model. We pre-train network-based user embeddings
and leverage a cross-attention mechanism between users and posts, together with
a community-enhanced vulnerability propagation (CVP) method to improve user and
propagation graph representations. Furthermore, we employ two multi-task
training strategies to mitigate negative transfer effects among tasks in
different settings, enhancing the overall performance of our approach. We also
construct two datasets with ground-truth annotations on information virality
and user vulnerability in rumor and non-rumor events, which are automatically
derived from existing rumor detection datasets. Extensive evaluation results of
our joint learning model confirm its superiority over strong baselines in all
three tasks: rumor detection, virality prediction, and user vulnerability
scoring. For instance, compared to the best baselines based on the Weibo
dataset, our model makes 3.8\% and 3.0\% improvements on Accuracy and MacF1 for
rumor detection, and reduces mean squared error (MSE) by 23.9\% and 16.5\% for
virality prediction and user vulnerability scoring, respectively. Our findings
suggest that our approach effectively captures the correlation between rumor
virality and user vulnerability, leveraging this information to improve
prediction performance and provide a valuable tool for infodemic surveillance.

在信息泛滥的时代，我们提出了一种新颖的方法来预测病毒谣言和易受攻击的用户，该方法利用了图神经网络模型、多任务训练策略和数据集构建，实现了在谣言检测、病毒性预测和用户脆弱性评分等任务上的卓越性能提升。

预测病毒性谣言和脆弱用户的信息传播监测

Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance

Dance challenges are going viral in video communities like TikTok nowadays.
Once a challenge becomes popular, thousands of short-form videos will be
uploaded in merely a couple of days. Therefore, virality prediction from dance
challenges is of great commercial value and has a wide range of applications,
such as smart recommendation and popularity promotion. In this paper, a novel
multi-modal framework which integrates skeletal, holistic appearance, facial
and scenic cues is proposed for comprehensive dance virality prediction. To
model body movements, we propose a pyramidal skeleton graph convolutional
network (PSGCN) which hierarchically refines spatio-temporal skeleton graphs.
Meanwhile, we introduce a relational temporal convolutional network (RTCN) to
exploit appearance dynamics with non-local temporal relations. An attentive
fusion approach is finally proposed to adaptively aggregate predictions from
different modalities. To validate our method, we introduce a large-scale viral
dance video (VDV) dataset, which contains over 4,000 dance clips of eight viral
dance challenges. Extensive experiments on the VDV dataset demonstrate the
efficacy of our model. Extensive experiments on the VDV dataset well
demonstrate the effectiveness of our approach. Furthermore, we show that short
video applications like multi-dimensional recommendation and action feedback
can be derived from our model.

本研究使用多种视觉和场景信息，提出了一个新的多模态框架用于全面预测舞蹈挑战的传播趋势。使用金字塔骨骼图卷积网络（PSGCN）模拟身体动作，并引入关系时间卷积网络（RTCN）来利用非局部时间关系进行外观动态建模。提出了一种关注融合方法，最终从不同模态自适应聚合预测。对大规模病毒舞蹈视频数据集进行了实验验证，证明了我们模型的有效性。另外，展示了该模型的多维度推荐和行动反馈等应用。

你会变得流行吗？学习预测舞蹈片段的病毒性

Will You Ever Become Popular? Learning to Predict Virality of Dance  Clips

The analysis of the creation, mutation, and propagation of social media
content on the Internet is an essential problem in computational social
science, affecting areas ranging from marketing to political mobilization. A
first step towards understanding the evolution of images online is the analysis
of rapidly modifying and propagating memetic imagery or `memes'. However, a
pitfall in proceeding with such an investigation is the current incapability to
produce a robust semantic space for such imagery, capable of understanding
differences in Image Macros. In this study, we provide a first step in the
systematic study of image evolution on the Internet, by proposing an algorithm
based on sparse representations and deep learning to decouple various types of
content in such images and produce a rich semantic embedding. We demonstrate
the benefits of our approach on a variety of tasks pertaining to memes and
Image Macros, such as image clustering, image retrieval, topic prediction and
virality prediction, surpassing the existing methods on each. In addition to
its utility on quantitative tasks, our method opens up the possibility of
obtaining the first large-scale understanding of the evolution and propagation
of memetic imagery.

通过深度学习和稀疏表示算法解耦图像数据并生成语义嵌入，通过图像聚类、图像检索、主题预测和病毒性预测等任务展示了该方法在 meme 和 Image Macros 等方面的优越性。