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Continuous-time dynamics models, e.g., neural ordinary differential equations, enable accurate modeling of underlying dynamics in time-series data. However, employing neural networks for parameterizing dynamics makes it challenging for humans to identify dependence structures, especially in the presence of delayed effects. In consequence, these models are not an attractive option when capturing dependence carries more importance than accurate modeling, e.g., in tsunami forecasting. In this paper, we present a novel method for identifying dependence structures in continuous-time dynamics models. We take a two-step approach: (1) During training, we promote weight sparsity in the model’s first layer during training. (2) We prune the sparse weights after training to identify dependence structures. In evaluation, we test our method in scenarios where the exact dependence structures of time-series are known. Compared to baselines, our method is more effective in uncovering dependence structures in data even when there are delayed effects. Moreover, we evaluate our method to a real-world tsunami forecasting, where the exact dependence structures are unknown beforehand. Even in this challenging scenario, our method still effective learns physically-consistent dependence structures and achieves high accuracy in forecasting. 

Disinformation activities that aim to manipulate public opinion pose serious challenges to managing online platforms. One of the most widely used disinformation techniques is bot-assisted fake social engagement, which is used to falsely and quickly amplify the salience of information at scale. Based on agenda-setting theory, we hypothesize that bot-assisted fake social engagement boosts public attention in the manner intended by the manipulator. Leveraging a proven case of bot-assisted fake social engagement operation in a highly trafficked news portal, this study examines the impact of fake social engagement on the digital public’s news consumption, search activities, and political sentiment. For that purpose, we used ground-truth labels of the manipulator’s bot accounts, as well as real-time clickstream logs generated by ordinary public users. Results show that bot-assisted fake social engagement operations disproportionately increase the digital public’s attention to not only the topical domain of the manipulator’s interest (i.e., political news) but also to specific attributes of the topic (i.e., political keywords and sentiment) that align with the manipulator’s intention. We discuss managerial and policy implications for increasingly cluttered online platforms. 

Recent policy initiatives have acknowledged the importance of disaggregating data pertaining to diverse Asian ethnic communities to gain a more comprehensive understanding of their current status and to improve their overall well-being. However, research on anti-Asian racism has thus far fallen short of properly incorporating data disaggregation practices. Our study addresses this gap by collecting 12-month-long data from X (formerly known as Twitter) that contain diverse sub-ethnic group representations within Asian communities. In this dataset, we break down anti-Asian toxic messages based on both temporal and ethnic factors and conduct a series of comparative analyses of toxic messages, targeting different ethnic groups. Using temporal persistence analysis, 𝑛-gram-based correspondence analysis, and topic modeling, this study provides compelling evidence that anti-Asian messages comprise various distinctive narratives. Certain messages targeting sub-ethnic Asian groups entail different topics that distinguish them from those targeting Asians in a generic manner or those aimed at major ethnic groups, such as Chinese and Indian. By introducing several techniques that facilitate comparisons of online anti-Asian hate towards diverse ethnic communities, this study highlights the importance of taking a nuanced and disaggregated approach for understanding racial hatred to formulate effective mitigation strategies. 

Recent works have shown that physics-inspired architectures allow the training of deep graph neural networks (GNNs) without oversmoothing. The role of these physics is unclear, however, with successful examples of both reversible (e.g., Hamiltonian) and irreversible (e.g., diffusion) phenomena producing comparable results despite diametrically opposed mechanisms, and further complications arising due to empirical departures from mathematical theory. This work presents a series of novel GNN architectures based upon structure preserving bracket-based dynamical systems, which are provably guaranteed to either conserve energy or generate positive dissipation with increasing depth. It is shown that the theoretically principled framework employed here allows for inherently explainable constructions, which contextualize departures from theory in current architectures and better elucidate the roles of reversibility and irreversibility in network performance. Code is available at the Github repository https://github.com/natrask/BracketGraphs.