Dispute Resolution in International Project Finance Transactions

This essay discusses how the legal practice in international financial problems has slowly evolved towards a better recognition of international arbitration in the field of project financing. While it is useful to compare the different types of dispute resolution mechanisms that are to be considered by participants for the implementation of their contracts, it is this author\u27s view that international arbitration is the most effective means of resolving international project finance transactions. Indeed, the assessment of the most effective forum cannot dismiss what this author considers as an essential feature of international project financing, i.e., its transactional unity. As a result, international arbitration is the most appropriate mechanism to deal with corollary specificities of international project financing, such as multi-party disputes. The business, and possibly, legal unity of international project finance transactions therefore determines the resolution of the disputes arising with respect to those transactions

Dispute Resolution in International Project Finance Transactions

This essay discusses how the legal practice in international financial problems has slowly evolved towards a better recognition of international arbitration in the field of project financing. While it is useful to compare the different types of dispute resolution mechanisms that are to be considered by participants for the implementation of their contracts, it is this author\u27s view that international arbitration is the most effective means of resolving international project finance transactions. Indeed, the assessment of the most effective forum cannot dismiss what this author considers as an essential feature of international project financing, i.e., its transactional unity. As a result, international arbitration is the most appropriate mechanism to deal with corollary specificities of international project financing, such as multi-party disputes. The business, and possibly, legal unity of international project finance transactions therefore determines the resolution of the disputes arising with respect to those transactions

A community role approach to assess social capitalists visibility in the Twitter network

In the context of Twitter, social capitalists are specific users trying to increase their number of followers and interactions by any means. These users are not healthy for the service, because they are either spammers or real users flawing the notions of influence and visibility. Studying their behavior and understanding their position in Twit-ter is thus of important interest. It is also necessary to analyze how these methods effectively affect user visibility. Based on a recently proposed method allowing to identify social capitalists, we tackle both points by studying how they are organized, and how their links spread across the Twitter follower-followee network. To that aim, we consider their position in the network w.r.t. its community structure. We use the concept of community role of a node, which describes its position in a network depending on its connectiv-ity at the community level. However, the topological measures originally defined to characterize these roles consider only certain aspects of the community-related connectivity, and rely on a set of empirically fixed thresholds. We first show the limitations of these measures, before extending and generalizing them. Moreover, we use an unsupervised approach to identify the roles, in order to provide more flexibility relatively to the studied system. We then apply our method to the case of social capitalists and show they are highly visible on Twitter, due to the specific roles they hold.Comment: arXiv admin note: substantial text overlap with arXiv:1406.661

Detecting Real-World Influence Through Twitter

In this paper, we investigate the issue of detecting the real-life influence of people based on their Twitter account. We propose an overview of common Twitter features used to characterize such accounts and their activity, and show that these are inefficient in this context. In particular, retweets and followers numbers, and Klout score are not relevant to our analysis. We thus propose several Machine Learning approaches based on Natural Language Processing and Social Network Analysis to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art ranking methods.Comment: 2nd European Network Intelligence Conference (ENIC), Sep 2015, Karlskrona, Swede