This study investigates the possibility of implementing national psychological defence measures utilizing social media. These measures are understood as an exercise of political power and are contextualised in the contemporary global information arena using Castells theory of communication power in the network society, while employing PSYOPS methodology to further understand the tactical dimensions. It also attempts to evaluate the prevalence of ”filter bubbles” and the potential hindrance such may be to successful implementation. Results show that key audiences can be reached and effectively influenced through the use of social media advert targeting systems and open source, fact-based information campaigns.

Surprise is an important component of serendipity. In this research, we address the problem of measuring the capacity of a recommender system at embedding surprise in its recommendations. We show that changes in surprise of an item owing to the growth in user experience, as well as to the increase in the number of items in the repository, are not taken into account by the current metrics and evaluation methods. As a result, in so far as the time elapsed between two measurements grows, they become increasingly incommensurable. This poses as an additional challenge in the assessment of the degree to which a recommender is exposed to unfavourable conditions, such as over-specialisation or filter bubble. We argue that a) surprise is a finite resource in any recommender system, b) there are limits to the amount of surprise that can be embedded in a recommendation, and c) these limits allow us to create a scale up in which two measurements that were taken at different moments can be directly compared. By adopting these ideas as premises, we applied the deductive method to define the concepts of maximum and minimum potential surprises and designed a surprise metric called \"normalised surprise\" that employs these limits. Our main contribution is an evaluation method that estimates the normalised surprise of a system. Four experiments were conducted to test the proposed metrics. The aim of the first and the second experiments was to validate the quality of the estimates of minimum and maximum potential surprise values obtained by means of a greedy algorithm. The first experiment employed a synthetic dataset to explore the limits to surprise to a user, and the second one employed the Movielens-1M to explore the limits to surprise that can be embedded in a recommendation list. The third experiment also employed the Movielens-1M dataset and was designed to investigate the effect that changes in item representation and item comparison exert on surprise. Finally, the fourth experiment compares the proposed and the current state-of-the-art evaluation method in terms of their results and execution times. The results obtained from the experiments a) confirm that the quality of the estimates of potential surprise are adequate for the purpose of evaluating normalised surprise; b) show that the item representation and comparison model that is adopted has a strong effect on surprise; and c) indicate an association between high degrees of surprise and negatively skewed pairwise distance distributions, and also indicate a significant difference in the average normalised surprise of recommendations produced by a factorisation algorithm when the surprise employs the cosine or the Euclidean distance / A surpresa é um componente importante da serendipidade. Nesta pesquisa, abordamos o problema de medir a capacidade de um sistema de recomendação de incorporar surpresa em suas recomendações. Mostramos que as mudanças na surpresa de um item, devidas ao crescimento da experiência do usuário e ao aumento do número de itens no repositório, não são consideradas pelas métricas e métodos de avaliação atuais. Como resultado, na medida em que aumenta o tempo decorrido entre duas medições, essas se tornam cada vez mais incomensuráveis. Isso se apresenta como um desafio adicional na avaliação do grau em que um sistema de recomendação está exposto a condições desfavoráveis como superespecialização ou filtro invisível. Argumentamos que a) surpresa é um recurso finito em qualquer sistema de recomendação; b) há limites para a quantidade de surpresa que pode ser incorporada em uma recomendação; e c) esses limites nos permitem criar uma escala na qual duas medições que foram tomadas em momentos diferentes podem ser comparadas diretamente. Ao adotar essas ideias como premissas, aplicamos o método dedutivo para definir os conceitos de surpresa potencial máxima e mínima e projetar uma métrica denominada \"surpresa normalizada\", que emprega esses limites. Nossa principal contribuição é um método de avaliação que estima a surpresa normalizada de um sistema. Quatro experimentos foram realizados para testar as métricas propostas. O objetivo do primeiro e do segundo experimentos foi validar a qualidade das estimativas de surpresa potencial mínima e máxima obtidas por meio de um algoritmo guloso. O primeiro experimento empregou um conjunto de dados sintético para explorar os limites de surpresa para um usuário, e o segundo empregou o Movielens-1M para explorar os limites da surpresa que pode ser incorporada em uma lista de recomendações. O terceiro experimento também empregou o conjunto de dados Movielens-1M e foi desenvolvido para investigar o efeito que mudanças na representação de itens e na comparação de itens exercem sobre a surpresa. Finalmente, o quarto experimento compara os métodos de avaliação atual e proposto em termos de seus resultados e tempos de execução. Os resultados que foram obtidos dos experimentos a) confirmam que a qualidade das estimativas de surpresa potencial são adequadas para o propósito de avaliar surpresa normalizada; b) mostram que o modelo de representação e comparação de itens adotado exerce um forte efeito sobre a surpresa; e c) apontam uma associação entre graus de surpresa elevados e distribuições assimétricas negativas de distâncias, e também apontam diferenças significativas na surpresa normalizada média de recomendações produzidas por um algoritmo de fatoração quando a surpresa emprega a distância do cosseno ou a distância Euclidiana

Copyright is conventionally justified as an incentive to produce and disseminate works of authorship. We can justify and theorise copyright more richly, not least because empirical evidence does not support the incentive narrative. Rather than focussing on quantitative matters such as the number of works incentivised and produced, we should consider copyright's qualitative influence on culture. A threshold objection to such an approach is the risk of cultural paternalism. This objection can be overcome. Rather than specifying paternalistic standards of merit for works, we can target the conditions under which their creation and consumption takes place. I argue, firstly, that we should adopt the following high-level principles: (i) that the conditions of creation and consumption of works should be conducive to democratic deliberation (democracy) and (ii) that they should facilitate the development of human capabilities (autonomy). Secondly, I propose that we pursue three mid-level objectives, which are helpful indicia of democracy and autonomy: - a fair and wide distribution of communicative and cultural power (inclusiveness); - diversity in the content and perspectives available to the public (diversity); and - conditions that permit authors and users of works to engage rigorously with the conventions of the media in which they operate (rigour). It is often said that copyright obstructs important qualitative objectives, like freedom of expression, and that we could better pursue these goals by weakening copyright and relying on non-proprietary alternatives. My approach produces a more optimistic, but also more complicated, view of copyright. While copyright's qualitative influence is not optimal, reductions in the strength and scope of copyright sometimes produces conditions and incentive structures that are worse for inclusiveness, diversity and rigour than stronger copyright. For example, both attention and wealth are highly concentrated in networked information economies driven by free sharing of content, and this is bad for diversity or inclusiveness. Online business models, based on surveillance of users' consumption of free works, are corrosive of autonomy and democracy. Merely removing copyright-based restrictions on the sharing of works is not a panacea for copyright's ills. A qualitative theory such as mine equips us to better understand and calibrate more richly the trade-offs involved in copyright policy decisions, and encourages us to treat copyright as part of a broader, qualitatively-oriented information and cultural policy.