Suchergebnisse

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.

Zum WerkInfluencerinnen und Influencer entfalten eine immer größere wirtschaftliche Bedeutung. Schätzungen zufolge erzielen in Deutschland inzwischen weit über 3 Millionen Menschen Einnahmen aus dem Erstellen von Inhalten für Social-Media-Plattformen. Daher ist es nicht überraschend, dass auch der Fiskus vermehrt ein Auge auf die wirtschaftliche Betätigung dieser Personengruppe wirft.Das vorliegende Werk stellt dar, welche steuerlichen Besonderheiten (bei der Beratung) von Influencerinnen und Influencern zu beachten sind.Vorteile auf einen Blickschneller Zugang zu den steuerlichen Problemstellungen bei Influencer-Mandatenzahlreiche Beispiele liefern praxisnahe Hilfe bei der steuerlichen Betreuung von Influencerinnen und Influencerndurch den Glossar-Aufbau schneller Zugriff auf die gesuchte steuerliche FragestellungZielgruppeFür Beraterinnen und Berater, Studierende, aber auch für alle, die als Influencerin oder Influencer tätig sind oder planen tätig zu werden

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

Changing hearts is an important part of changing minds. Research shows that appealing to human emotion can help you make your case and build your authority as a leader. This book highlights that research and shows you how to act on it, presenting both comprehensive frameworks for developing influence and small, simple tactics you can use to convince others every day. This volume includes the work of: Nick Morgan, Robert Cialdini, Linda A. Hill, and Nancy Duarte. This collection of articles includes "Understand the Four Components of Influence," by Nick Morgan; "Harnessing the Science of Persuasion," by Robert Cialdini; "Three Things Managers Should Be Doing Every Day," by Linda A. Hill and Kent Lineback; "Learning Charisma," by John Antonakis, Marika Fenley, and Sue Liechti; "To Win People Over, Speak to Their Wants and Needs," by Nancy Duarte; "Storytelling That Moves People," an interview with Robert McKee by Bronwyn Fryer; "The Surprising Persuasiveness of a Sticky Note," by Kevin Hogan; and "When to Sell with Facts and Figures, and When to Appeal to Emotions," by Michael D. Harris.

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.

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.

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.