Microblogging platforms like X (formerly Twitter) and Sina Weibo have become key channels for spreading information online. Accurately predicting information spread, such as users’ reposting activities, is essential for applications including content recommendation and analyzing public sentiment. Current advanced models rely on deep representation learning to extract features from various inputs, such as users’ social connections and repost history, to forecast reposting behavior. Nonetheless, these models frequently ignore intrinsic confounding factors, which may cause the models to capture spurious relationships, ultimately impacting prediction performance. To address this limitation, we propose a novel Debiased Reposting Prediction model (DRP). Our model mitigates the influence of confounding variables by incorporating intervention operations from causal inference, enabling it to learn the causal associations between features and user reposting behavior. Specifically, we introduce a memory network within DRP to enhance the model’s perception of confounder distributions. This network aggregates and learns confounding information dispersed across different training data batches by optimizing the reconstruction loss. Furthermore, recognizing the challenge of acquiring prior knowledge of causal graphs, which is crucial for causal inference, we develop a causal discovery module within DRP (CD-DRP). This module allows the model to autonomously uncover the causal graph of feature variables by analyzing microblogging data. Experimental results on multiple real-world datasets demonstrate that our proposed method effectively uncovers causal relationships between variables, exhibits strong time efficiency, and outperforms state-of-the-art models in prediction performance (improved by 2.54%) and overfitting reduction (by 7.44%).

Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development. Traditional neural network methods, such as BiLSTM, could be ineffective due to the lack of lab data for model training and the overshadowing of crucial features within sequence concatenation. The current work proposes a less data-consuming model incorporating a pre-trained gene sequence model and a mutual information inference operator. Our methodology utilizes gene alignment and deduplication algorithms to preprocess gene sequences, enhancing the model’s capacity to discern and focus on distinctions among input gene pairs. The model, i.e., DNA Pretrained Cross-Immunity Protection Inference model (DPCIPI), outperforms state-of-the-art (SOTA) models in predicting hemagglutination inhibition titer from influenza viral gene sequences only. Improvement in binary cross-immunity prediction is 1.58% in F1, 2.34% in precision, 1.57% in recall, and 1.57% in Accuracy. For multilevel cross-immunity improvements, the improvement is 2.12% in F1, 3.50% in precision, 2.19% in recall, and 2.19% in Accuracy. Our study showcases the potential of pre-trained gene models to improve predictions of antigenic variation and cross-immunity. With expanding gene data and advancements in pre-trained models, this approach promises significant impacts on vaccine development and public health.