The epidemic of infectious diseases has a significant impact on society, the economy, and people’s lives. Social media, with its high user participation and rapid information dissemination, plays a crucial role in shaping public opinion. Fine-grained sentiment analysis of public opinion on infectious diseases can provide valuable insights for improving the quality of public services. However, there are few relevant studies on Chinese data due to language complexity and low resources. Moreover, most of the existing approaches utilize the Graph Neural Network (GCN) method by syntactic dependency trees to construct graphs of text, which ignore the potential link relationships between aspects and words. Therefore, to address this limitation, in this article, we propose a new method based on GCN using aspect-specific heterogeneous graphs, named ASHGCN, which combines BiLSTM, heterogeneous graphs, GCN, the mask and the attention mechanism. We mine social media posts related to COVID-19 for aspect-based sentiment analysis task (ABSA) for ten aspect entity types in both Chinese and English data. The heterogeneous graph is designed with two node types (aspect nodes and non-aspect nodes) and four edge connection types, including various relationships between aspect entities, and between aspect entities and non-aspect entities. In addition, we release a Chinese dataset and an English dataset that include medical and named entities, along with corresponding sentiment labels. Experiments on our datasets, as well as two public datasets, demonstrate that our method greatly improves performance in the ABSA task. Ablation experiments and case studies further support the effectiveness of the proposed approach.

Predicting the popularity of information in social networks is crucial for effective social marketing and recommendation systems. However, accurately comprehending the complex dynamics of information diffusion remains a challenging task. Existing methods, including feature-based approaches, point process models, and deep learning techniques, often fail to capture the fine-grained features of information cascades, such as dynamic diffusion patterns, cascade statistics, and the interplay between spatial and temporal information. To address these limitations, we propose Casformer, a novel graph-based Transformer architecture that effectively learns both micro-level time-aware structural information and macro-level long-term influence along the information propagation process. Casformer employs a cascade attention network (CAT) to capture the micro-level features and a Transformer model to learn the macro-level influence. Furthermore, we introduce an adaptive cascade graph sampling strategy based on the temporal diffusion pattern and cascade statistics of information to obtain the most informative cascade graph sequence. By leveraging multi-level fine-grained evolving features of information cascades, Casformer achieves high accuracy in information popularity prediction. Experimental results on real-world social network and scientific citation network datasets demonstrate the effectiveness and superiority of Casformer compared to state-of-the-art methods in information popularity prediction.

Data mining in infectious disease pandemic scenarios is a complex giant task involving data from various fields and requirements of real-time and dynamic. In this paper, we propose a graph mining system for the infectious disease pandemic, IDGMS, with one-stop, dynamic, and interactive characteristics. The system has been applied to solve problems from three view scales and performs well. The system is constructed as a loose coupling structure at the front and back ends and can be extended to more graph mining issues. To the best of our knowledge, we are the first graph system especially targeting data mining of infectious diseases.

In this poster, we propose a novel, effective tuple-based anonymization technique for categorical data over the Internet. By utilizing a new structure, called Candidate Encoding Sequence with Frequency, and a set of new rules for generating such a sequence for each domain value of the categorical data, we can effectively solve the key limitation of the existing methods. Our experimental results demonstrate the superiority of our method against the existing method in terms of the strength of privacy protection.