Social media has become an essential channel for posting disaster-related information, which provides governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention, and extracting useful information is still challenging. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes toward disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41,993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response, and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models. To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of peoples' behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management.

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We investigated how transmission of hunger- and satiety-promoting neuropeptides, NPY and αMSH, is integrated at the level of intracellular signaling to control feeding. Receptors for these peptides use the second messenger cAMP. How cAMP integrates opposing peptide signals to regulate energy balance, and the in vivo spatiotemporal dynamics of endogenous peptidergic signaling, remain largely unknown. We show that AgRP axon stimulation in the paraventricular hypothalamus evokes probabilistic NPY release that triggers stochastic cAMP decrements in downstream MC4R-expressing neurons (PVHMC4R). Meanwhile, POMC axon stimulation triggers stochastic, αMSH-dependent cAMP increments. Release of either peptide impacts a ~100 µm diameter region, and when these peptide signals overlap, they compete to control cAMP. The competition is reflected by hunger-state-dependent differences in the amplitude and persistence of cAMP transients: hunger peptides are more efficacious in the fasted state, satiety peptides in the fed state. Feeding resolves the competition by simultaneously elevating αMSH release and suppressing NPY release, thereby sustaining elevated cAMP in PVHMC4R neurons. In turn, cAMP potentiates feeding-related excitatory inputs and promotes satiation across minutes. Our findings highlight how biochemical integration of opposing, quantal peptide signals during energy intake orchestrates a gradual transition between stable states of hunger and satiety.