In this paper, we perform a large-scale study of the Twitter follower network, involving around 0.42 million users who justify drug abuse, to characterize the spreading of drug abuse tweets across the network. Our observations reveal the existence of a very large giant component involving 99% of these users with dense local connectivity that facilitates the spreading of such messages. We further identify active cascades over the network and observe that cascades of drug abuse tweets get spread over a long distance through the engagement of several closely connected groups of users. Moreover, our observations also reveal a collective phenomenon, involving a large set of active fringe nodes (with a small number of follower and following) along with a small set of well-connected non-fringe nodes that work together towards such spread, thus potentially complicating the process of arresting such cascades. Further, we discovered that the engagement of the users with respect to certain drugs like Vicodin, Percocet and OxyContin, that were observed to be most mentioned in Twitter, is instantaneous. On the other hand for drugs like Lortab, that found lesser mentions, the engagement probability becomes high with increasing exposure to such tweets, thereby indicating that drug abusers engaged on Twitter remain vulnerable to adopting newer drugs, aggravating the problem further.
Medical Trust-Network is one of the most promising fields of study in network science. Establishment of trust within medical entities ensures better treatment and increases better medical facilities. The word 'Trust' signifies a very important behavioral aspect between any human entities, especially among doctors and patients. To represent such relationships Trust Network Models are built to express the interactions between human entities within such networks. Though the idea of a Trust-Network has traditionally been one of the major areas of research, yet the concept of a medical trust network model is relatively a new domain. In this paper, we introduce an overall multilayered Trust Network to represent the entire healthcare architecture. More specifically our model is based on an evolutionary graph system with a discrete relationship between the three most important entities of any healthcare system, namely -- Doctors, Departments, and Hospitals. Observations indicate that based on our model, the medical healthcare system is a multilayered model unlike a feed-forward model as indicated by previous studies.
Medical Trust-Network is one of the most promising fields of study in network science. Establishment of trust within medical entities ensures better treatment and increases better medical facilities. The word ‘Trust’ signifies a very important behavioral aspect between any human entities, especially among doctors and patients. To represent such relationships Trust Network Models are built to express the interactions between human entities within such networks. Though the idea of a Trust-Network has traditionally been one of the major areas of research, yet the concept of a medical trust network model is relatively a new domain. In this paper, we introduce an overall multilayered Trust Network to represent the entire healthcare architecture. More specifically our model is based on an evolutionary graph system with a discrete relationship between the three most important entities of any healthcare system, namely – Doctors, Departments, and Hospitals. Observations indicate that based on our model, the medical healthcare system is a multilayered model unlike a feed-forward model as indicated by previous studies.
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