Influence Stakeholders, Influence the World
In: Michael L. Barnett. Influence Stakeholders, Influence the World. Research in the Sociology of Organizations. Edited by: F. Briscoe, B. King & J. Leitzinger (2018 Forthcoming)
In: Michael L. Barnett. Influence Stakeholders, Influence the World. Research in the Sociology of Organizations. Edited by: F. Briscoe, B. King & J. Leitzinger (2018 Forthcoming)
SSRN
This paper qualitatively looks at the relationship between Community relations and rural development with special reference to oil producing communities in the Niger-Delta. It points out the adverse effects of oil exploitation and exploration by the multinational oil companies on the oil producing rural communities of the Niger-Delta. The oil producing rural communities are faced with mass unemployment due to the problem of environmental degradation, ecological problems are not adequately addressed, roads, electricity, water, education and health facilities are in terrible condition, youth restiveness and all sorts of crimes due to little or no community relations; thereby making the condition of living not conducive for the local people. This negligence has left the operations of the multinational oil companies in chaos due to the activities of the restive youths. As checks to these developmental challenges the oil companies consciously carried out some developmental works in the oil producing rural communities yet the problems of pipeline bombing, vandalism, etc continues. The paper believes the multinational developmental efforts were not appreciated because the power structures of the oil producing rural communities were not consulted. The paper therefore stressed the need for consultation to determine the felt-needs of the local people to foster good neighbourliness between the oil companies and the oil producing communities.
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In: Temple International & Comparative Law Journal, Band 32, Heft 1
SSRN
In: Twists of Fate, S. 107-125
In: The journal of conflict resolution: journal of the Peace Science Society (International), Band 38, Heft 4, S. 665-689
ISSN: 1552-8766
One justification for U.S. arms transfers is that the United States can manipulate its arms exports to make the recipients of American aid comply with American wishes. This article explores the conditions under which such arms influence attempts succeed. Sixteen potential determinants are discussed, drawn from the attributes of the influence attempt, the recipient, the interaction of the recipient and supplier, the supplier, and the systemic environment. A data set of 191 American arms influence attempts from 1950 to 1992 is presented. Using logit analysis, the variables are tested against the outcome—success or failure—of the influence attempt. Successful influence attempts are more likely when the United States used promises or rewards, focused on altering the recipient's foreign policy, made the attempt on civilian regimes, supplied more of the recipient's arms, and made attempts in the first half of the cold war era, when the United States was generally more powerful.
In: The annals of the American Academy of Political and Social Science, Band 608, S. 233-250
ISSN: 1552-3349
This article looks back at the publication of Personal Influence (Katz & Lazarsfeld 1955) to bring into focus the multistranded history of discussion & debate over the mass media audience during the twentieth century. In contrast with the heroic narrative, constructed retrospectively, that prioritizes cultural studies' approaches to audiences, the author suggests that this rich & interdisciplinary history offers many fruitful ways forward as the agenda shifts from mass media to new media audiences. Although audience research has long been characterized by struggles between critical & administrative schools of communication, & between opposed perspectives on the relation of the individual to society, Katz & Lazarsfeld's work, & subsequent work by Katz & his collaborators, suggests possibilities for convergence, or at least productive dialogue, across hitherto polarized perspectives as researchers collectively seek to understand how, in their everyday lives, people can, & could, engage with media to further democratic participation in the public sphere. References. [Reprinted by permission of Sage Publications Inc., copyright 2006 The American Academy of Political and Social Science.]
In: Political and Civic Leadership: A Reference Handbook, S. 742-749
In: Evaluation and program planning: an international journal, Band 92, S. 102091
ISSN: 1873-7870
In: Kim , J 2018 , ' The Influence of EU Agencies : Real but guided influence in the policy-making process ' , Maastricht University , Maastricht . https://doi.org/10.26481/dis.20181211jk
This PhD research investigates de facto influence of EU agencies on the development of EU policies. Based on a case-study approach and expert interviews, the European Centre for Disease Prevention and Control as well as the European Chemicals Agency are analyzed. The main findings are that EU agencies, albeit to varying degrees, do influence the content of policy proposals developed by the European Commission. However, the Commission does fully exercise its formal power to decide on the content of proposals. Only under willing guidance by the Commission, do EU agencies increase influence. While European governance inevitably involves technocratic characteristics, it should not be directly interpreted as a deficit of democracy or legitimacy in the EU.
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At the end students will be able to:• Compare the differences between influence and lobbying in a democratic and digi-tal context,• Reveal the psychological dimensions of influence and its expansion to digital tools,• Identify the protagonists and their activities,• Elaborate strong arguments relying on data and expert opinions,• Describe the process for elaborating norms and laws,• Discover the public action process, in a parliament as well as in civil society,• Ask ethical and critical questions about lobbying regulations.Course requirements: basic notions on the most important theories of information and communication, and competences in sociology. A good knowledge of actuality is necessary. ; info:eu-repo/semantics/published
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International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.
BASE
International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.
BASE
International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.
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