1,281 research outputs found
Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance
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.Comment: Accepted by IP&
Predicting Successful Memes using Network and Community Structure
We investigate the predictability of successful memes using their early
spreading patterns in the underlying social networks. We propose and analyze a
comprehensive set of features and develop an accurate model to predict future
popularity of a meme given its early spreading patterns. Our paper provides the
first comprehensive comparison of existing predictive frameworks. We categorize
our features into three groups: influence of early adopters, community
concentration, and characteristics of adoption time series. We find that
features based on community structure are the most powerful predictors of
future success. We also find that early popularity of a meme is not a good
predictor of its future popularity, contrary to common belief. Our methods
outperform other approaches, particularly in the task of detecting very popular
or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and social media (ICWSM 2014
Multi-scale graph capsule with influence attention for information cascades prediction
Information cascade size prediction is one of the primary challenges for understanding the diffusion of information. Traditional feature-based methods heavily rely on the quality of handcrafted features, requiring extensive domain knowledge and hard to generalize to new domains. Recently, inspired by the success of deep learning in computer vision and natural language processing, researchers have developed neural network-based approaches for tackling this problem. However, existing deep learning-based methods either focused on modeling the temporal characteristics of cascades but ignored the structural information or failed to take the order-scale and position-scale into consideration in modeling structures of information propagation. This paper proposed a novel graph neural network-based model, called MUCas, to learn the latent representations of cascade graphs from a multi-scale perspective, which can make full use of the direction-scale, high-order-scale, position-scale, and dynamic-scale of cascades via a newly designed MUlti-scale Graph Capsule Network (MUG-Caps) and the influence-attention mechanism. Extensive experiments conducted on two real-world data sets demonstrate that our MUCas significantly outperforms the state-of-the-art approaches.Computer Science
MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE
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