Studying Paths of Participation in Viral Diffusion Process

Authors propose a conceptual model of participation in viral diffusion process composed of four stages: awareness, infection, engagement and action. To verify the model it has been applied and studied in the virtual social chat environment settings. The study investigates the behavioral paths of actions that reflect the stages of participation in the diffusion and presents shortcuts, that lead to the final action, i.e. the attendance in a virtual event. The results show that the participation in each stage of the process increases the probability of reaching the final action. Nevertheless, the majority of users involved in the virtual event did not go through each stage of the process but followed the shortcuts. That suggests that the viral diffusion process is not necessarily a linear sequence of human actions but rather a dynamic system.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Modelling the dynamics of the students academic performance in the German region of North Rhine- Westphalia: an epidemiological approach with uncertainty

This is an author's accepted manuscript of an article published in "International Journal of Computer Mathematics"; Volume 91, Issue 2, 2014; copyright Taylor & Francis; available online at: http://dx.doi.org/10.1080/00207160.2013.813937Student academic underachievement is a concern of paramount importance in Europe, where around 15% of the students in the last high school courses do not achieve the minimum knowledge academic requirement. In this paper, we propose a model based on a system of differential equations to study the dynamics of the students academic performance in the German region of North Rhine-Westphalia. This approach is supported by the idea that both, good and bad study habits, are a mixture of personal decisions and influence of classmates. This model allows us to forecast the student academic performance by means of confidence intervals over the next few years.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness grant MTM2009-08587 and Universitat Politecnica de Valencia grant PAID06-11-2070.Cortés, J.; Ehrhardt, M.; Sánchez Sánchez, A.; Santonja, F.; Villanueva Micó, RJ. (2014). Modelling the dynamics of the students academic performance in the German region of North Rhine- Westphalia: an epidemiological approach with uncertainty. International Journal of Computer Mathematics. 91(2):241-251. https://doi.org/10.1080/00207160.2013.813937S241251912Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21(1), 243-247. doi:10.1007/bf02532251Brockwell, P. J., & Davis, R. A. (1996). Introduction to Time Series and Forecasting. Springer Texts in Statistics. doi:10.1007/978-1-4757-2526-1Dogan, G. (2007). Bootstrapping for confidence interval estimation and hypothesis testing for parameters of system dynamics models. System Dynamics Review, 23(4), 415-436. doi:10.1002/sdr.362Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1), 1-26. doi:10.1214/aos/1176344552LJUNG, G. M., & BOX, G. E. P. (1979). The likelihood function of stationary autoregressive-moving average models. Biometrika, 66(2), 265-270. doi:10.1093/biomet/66.2.265Martcheva, M., & Castillo-Chavez, C. (2003). Diseases with chronic stage in a population with varying size. Mathematical Biosciences, 182(1), 1-25. doi:10.1016/s0025-5564(02)00184-0J.D. Murray,Mathematical Biology, Springer, New York, 2002.Nelder, J. A., & Mead, R. (1965). A Simplex Method for Function Minimization. The Computer Journal, 7(4), 308-313. doi:10.1093/comjnl/7.4.308Yazici, B., & Yolacan, S. (2007). A comparison of various tests of normality. Journal of Statistical Computation and Simulation, 77(2), 175-183. doi:10.1080/10629360600678310M.Á.M. Zabal, P.F. Berrocal, C. Coll, and M. de los Ángeles Melero Zabal,La Interacción Social en Contextos Educativos[Social interaction in educational contexts], Psicología/Siglo XXI de España Editores Series, Siglo XXI de España, 1995

Manifesto of computational social science

The increasing integration of technology into our lives has created unprecedented volumes of data on society's everyday behaviour. Such data opens up exciting new opportunities to work towards a quantitative understanding of our complex social systems, within the realms of a new discipline known as Computational Social Science. Against a background of financial crises, riots and international epidemics, the urgent need for a greater comprehension of the complexity of our interconnected global society and an ability to apply such insights in policy decisions is clear. This manifesto outlines the objectives of this new scientific direction, considering the challenges involved in it, and the extensive impact on science, technology and society that the success of this endeavour is likely to bring about.The publication of this work was partially supported by the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 284709, a Coordination and Support Action in the Information and Communication Technologies activity area (‘FuturICT’ FET Flagship Pilot Project). We are grateful to the anonymous reviewers for the insightful comments.Publicad

Targeted Policy Making by Transforming Social Networks

Part 1: Research DirectionsInternational audienceCurrent economic conditions press governments worldwide to develop more efficient policies with significantly lower budgets. A possible way to achieve this is by exploiting online social networks. The tremendous impact of social networks in everyday life (e.g. obesity, financial situation, smoking etc.) is now well established in the literature. However, up to now, the impact of online social networks in policy making has not been thoroughly investigated. We claim that policies, in addition to their traditional aims, should also aim to improve the online connections of target population as this will enable more targeted thus more efficient and effective policy making. In this paper, we present this idea, relate it to traditional policy making lifecycles, and investigate relevant technological aspects. We anticipate this work will contribute to the on-going discussion on the pros and cons of exploiting online social networks in policy making