@article {Mityagin2020,
	title = {Forecasting of Population Narcotization under the Implementation of a Drug Use Reduction Policy},
	journal = {Complexity},
	volume = {2020},
	number = {9135024},
	year = {2020},
	pages = {1{\textendash}14},
	abstract = {In this paper, we present an approach to drug addiction simulation and forecasting in the medium and long terms in cities having a high population density and a high rate of social communication. Drug addiction forecasting is one of the basic components of the antidrug policy, giving informational and analytic support both at the regional and at the governmental level. However, views on the drug consumption problem vary in different regions, and as a consequence, several approaches to antidrug policy implementation exist. Thereby, notwithstanding the fact that the phenomenology of the population narcotization process is similar in the different regions, approaches to the modeling of drug addiction may also substantially differ for different kinds of antidrug policies. This paper presents a survey of the available antidrug policies and the corresponding approaches to the simulation of population narcotization. This article considers the approach to the construction of the regression model of anesthesia on the main components formed on the basis of indicators of social and economic development. The substantiation of the chosen method is given, which is associated with a significant correlation of indicators, which characterizes the presence of a small number of superfactors. This allows us to form a conclusion about the general level of development of the region as the main factor determining the drug addiction. A new model is proposed for one of the most widespread antidrug policies, namely, the drug use reduction policy. The model helps determine the significant factors of population narcotization and allows to estimate its damage. The model is tested successfully using St. Petersburg data.},
	doi = {10.1155/2020/9135024},
	author = {Mityagin, Sergey and Gershenson, Carlos and Boukhanovsky, Alexander}
}
@article {Pina-Garcia2018,
	title = {From neuroscience to computer science: a topical approach on Twitter},
	journal = {Journal of Computational Social Science},
	volume = {1},
	number = {1},
	year = {2018},
	pages = {187{\textendash}208},
	abstract = {Twitter is perhaps the most influential microblogging service, with 271 million regular users producing approximately 500 million tweets per day. Previous studies of tweets discussing scientific topics are limited to local surveys or may not be representative geographically. This indicates a need to harvest and analyse tweets with the aim of understanding the level of dissemination of science related topics worldwide. In this study, we use Twitter as case of study and explore the hypothesis of science popularization via the social stream. We present and discuss tweets related to popular science around the world using eleven keywords. We analyze a sample of 306,163 tweets posted by 91,557 users with the aim of identifying tweets and those categories formed around temporally similar topics. We systematically examined the data to track and analyze the online activity around users tweeting about popular science. In addition, we identify locations of high Twitter activity that occur in several places around the world. We also examine which sources (mobile devices, apps, and other social networks) are used to share popular science related links. Furthermore, this study provides insights into the geographic density of popular science tweets worldwide. We show that emergent topics related to popular science are important because they could help to explore how science becomes of public interest. The study also offers some important insights for studying the type of scientific content that users are more likely to tweet.},
	isbn = {2432-2725},
	doi = {10.1007/s42001-017-0002-9},
	url = {https://doi.org/10.1007/s42001-017-0002-9},
	author = {Pi{\~n}a-Garc{\'\i}a, C. A. and Siqueiros-Garc{\'\i}a, J. Mario and Robles-Belmont, E. and Carre{\'o}n, Gustavo and Gershenson, Carlos and L{\'o}pez, Julio Amador D{\'\i}az}
}
