Social media is an invaluable source of time-critical information during a crisis. However, emergency response and humanitarian relief organizations that would like to use this information struggle with an avalanche of social media messages that exceeds the human capacity to process. Emergency managers, decision makers, and affected communities can make sense of social media through a combination of machine computation and human compassion - expressed by thousands of digital volunteers who publish, process, and summarize potentially life-saving information. This book brings together computational methods from many disciplines: natural language processing, semantic technologies, data mining, machine learning, network analysis, human-computer interaction, and information visualization, focusing on methods that are commonly used for processing social media messages under time-critical constraints, and offering more than 500 references to in-depth information.
This research is an empirical study of the legitimacy of economic inequality with a focus on the case of Chile. Chile is an appealing case study in this regard because it has been one of the countries with the highest indexes of economic inequality over the past several decades. Theoretical perspectives based on the rational interest of the median voter have pointed out a negative association between high levels of inequality and legitimacy. Nevertheless, empirical evidence indicates that an unequal distribution of income is not necessarily challenged by the majority of a society, a phenomenon associated with the concept of legitimacy of economic inequality. Most empirical studies of this topic to date have considered social contexts that are not characterized by (comparatively) high levels of income inequality; thus, the impact of the level of inequality on its legitimacy remains largely unclear. The present study aimed at bridging this research gap, guided by the question: How do high levels of income inequality in a society influence the legitimacy of economic inequality? Using data obtained by comparative public opinion projects including the International Social Survey Program (ISSP) and the International Social Justice Project (ISJP), this research considered individual preferences for occupational earnings inequality (the just earnings gap) as the main object of study. The central hypothesis was that individual preferences are strongly influenced by contextual standards such as the current income distribution, leading individuals of countries with high levels of inequality to have stronger average preferences for economic inequality (the so-called existential argument). Empirical evidence of legitimacy was related to two central dimensions based on David Beetham's multidimensional concept of legitimacy: (a) consensus regarding the inequality in the distribution of earnings in Chile and (b) the impact of the country level of income inequality on individual preferences for a larger just earnings gap. The empirical analysis provided partial evidence regarding the consensus about inequality in Chile, whereas in an international comparative framework, countries with higher levels of income inequality showed a stronger preference for a larger just earnings gap.
It is the story of a youth music band, who in the late seventies and early eighties, begin to see the growth and subsequent success of their band. In this one stands out one of his new members called Mónica, who has had to suffer the consequences of a kidnapping when she was fourteen years old and who, according to the opinion of experts and musical critics, can become a great star. There is no lack of romance, jealousy, competition, problems and moments in which the most prominent members of the group must come to make crucial decisions, which will end up affecting the future of the band.
The book is a comprehensive, self-contained introduction to the mathematical modeling and analysis of disease transmission models. It includes (i) an introduction to the main concepts of compartmental models including models with heterogeneous mixing of individuals and models for vector-transmitted diseases, (ii) a detailed analysis of models for important specific diseases, including tuberculosis, HIV/AIDS, influenza, Ebola virus disease, malaria, dengue fever and the Zika virus, (iii) an introduction to more advanced mathematical topics, including age structure, spatial structure, and mobility, and (iv) some challenges and opportunities for the future. There are exercises of varying degrees of difficulty, and projects leading to new research directions. For the benefit of public health professionals whose contact with mathematics may not be recent, there is an appendix covering the necessary mathematical background. There are indications which sections require a strong mathematical background so that the book can be useful for both mathematical modelers and public health professionals.
Research on social networks has exploded over the last decade. To a large extent, this has been fueled by the spectacular growth of social media and online social networking sites, which continue growing at a very fast pace, as well as by the increasing availability of very large social network datasets for purposes of research. A rich body of this research has been devoted to the analysis of the propagation of information, influence, innovations, infections, practices and customs through networks. Can we build models to explain the way these propagations occur? How can we validate our models against any available real datasets consisting of a social network and propagation traces that occurred in the past? These are just some questions studied by researchers in this area. Information propagation models find applications in viral marketing, outbreak detection, finding key blog posts to read in order to catch important stories, finding leaders or trendsetters, information feed ranking, etc. A number of algorithmic problems arising in these applications have been abstracted and studied extensively by researchers under the garb of influence maximization. This book starts with a detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena. We describe their properties as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others. We delve deep into the key problem of influence maximization, which selects key individuals to activate in order to influence a large fraction of a network. Influence maximization in classic diffusion models including both the independent cascade and the linear threshold models is computationally intractable, more precisely #P-hard, and we describe several approximation algorithms and scalable heuristics that have been proposed in the literature. Finally, we also deal with key issues that need to be tackled in order to turn this research into practice, such as learning the strength with which individuals in a network influence each other, as well as the practical aspects of this research including the availability of datasets and software tools for facilitating research. We conclude with a discussion of various research problems that remain open, both from a technical perspective and from the viewpoint of transferring the results of research into industry strength applications.
Adversarial Web Search considers the effects of the adversarial relationship between search systems and those who wish to manipulate them, a field known as Adversarial Information Retrieval.
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