Catch me if you can: a participant-level rumor detection framework via fine-grained user representation learning

Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in the spreading chain. In this study, we propose a novel participant level rumor detection framework. It explicitly models and integrates various fine-grained user representations (i.e., user influence, susceptibility, and temporal information) of all participants from the propagation threads via deep representation learning. Experiments conducted on real world datasets demonstrate a significant accuracy improvement of our approach. Theoretically, we contribute to the effective usage of data science and analytics for social information diffusion design, particularly rumor detection. Practically, our results can be used to improve the quality of rumor detection services for social platforms.Computer Science

Multiplicity dependence of pion, kaon, proton and lambda production in p–Pb collisions at √sNN = 5.02 TeV

Inthis Letter, comprehensive results on π±,K±,K0S, p(pbar) and Λ(Λbar) production at mid-rapidity (0< yCMS < 0.5) in p–Pb collisions at √sNN = 5.02 TeV, measured by the ALICE detector at the LHC, are reported. The transverse momentum distributions exhibit a hardening as a function of event multiplicity, which is stronger for heavier particles. This behavior is similar to what has been observed in pp and Pb–Pb collisions at the LHC. The measured pT distributions are compared to d–Au, Au–Au and Pb–Pb results at lower energy and with predictions based on QCD-inspired and hydrodynamic models