Mining Reaction and Diffusion Dynamics in Social Activities

Large quantifies of online user activity data, such as weekly web search volumes, which co-evolve with the mutual influence of several queries and locations, serve as an important social sensor. It is an important task to accurately forecast the future activity by discovering latent interactions from such data, i.e., the ecosystems between each query and the flow of influences between each area. However, this is a difficult problem in terms of data quantity and complex patterns covering the dynamics. To tackle the problem, we propose FluxCube, which is an effective mining method that forecasts large collections of co-evolving online user activity and provides good interpretability. Our model is the expansion of a combination of two mathematical models: a reaction-diffusion system provides a framework for modeling the flow of influences between local area groups and an ecological system models the latent interactions between each query. Also, by leveraging the concept of physics-informed neural networks, FluxCube achieves high interpretability obtained from the parameters and high forecasting performance, together. Extensive experiments on real datasets showed that FluxCube outperforms comparable models in terms of the forecasting accuracy, and each component in FluxCube contributes to the enhanced performance. We then show some case studies that FluxCube can extract useful latent interactions between queries and area groups.Comment: Accepted by CIKM 202

The Chance of Winning Election Impacts on Social Media Strategy

Social media has been a paramount arena for election campaigns for political actors. While many studies have been paying attention to the political campaigns related to partisanship, politicians also can conduct different campaigns according to their chances of winning. Leading candidates, for example, do not behave the same as fringe candidates in their elections, and vice versa. We, however, know little about this difference in social media political campaign strategies according to their odds in elections. We tackle this problem by analyzing candidates' tweets in terms of users, topics, and sentiment of replies. Our study finds that, as their chances of winning increase, candidates narrow the targets they communicate with, from people in general to the electrical districts and specific persons (verified accounts or accounts with many followers). Our study brings new insights into the candidates' campaign strategies through the analysis based on the novel perspective of the candidate's electoral situation

Dynamic observation of a damping material using micro X-ray computed tomography coupled with a phase-locked loop

As rubber materials are used for damping, clarifying the relationship between the loss factor and microstructure would help develop high-performance damping materials. Although nondestructive observations using X-ray computed tomography (CT) under repetitive deformation have been reported, no observations have been reported at the submicron order that capture low-strain deformation, such as vibration exposure. The internal deformation behavior of materials with different loss factors has not yet been evaluated. This study proposes a dynamic X-ray CT method for specimens under tensile amplitudes, directly evaluating the internal deformation behavior of materials under dynamic conditions. The proposed 4D-CT has an excitation of 1 Hz and a spatial resolution of 0.5 μm. The local strain was obtained from X-ray CT at each phase, and the deformation behavior was evaluated. The results revealed that the peak of the local strain amplitude distribution curve decreased and the distribution widened as fine particles were mixed