False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

Sustainable seabed mining: guidelines and a new concept for Atlantis II Deep

The feasibility of exploiting seabed resources is subject to the engineering solutions, and economic prospects. Due to rising metal prices, predicted mineral scarcities and unequal allocations of resources in the world, vast research programmes on the exploration and exploitation of seabed minerals are presented in 1970s. Very few studies have been published after the 1980s, when predictions were not fulfilled. The attention grew back in the last decade with marine mineral mining being in research and commercial focus again and the first seabed mining license for massive sulphides being granted in Papua New Guinea’s Exclusive Economic Zone.Research on seabed exploitation and seabed mining is a complex transdisciplinary field that demands for further attention and development. Since the field links engineering, economics, environmental, legal and supply chain research, it demands for research from a systems point of view. This implies the application of a holistic sustainability framework of to analyse the feasibility of engineering systems. The research at hand aims to close this gap by developing such a framework and providing a review of seabed resources. Based on this review it identifies a significant potential for massive sulphides in inactive hydrothermal vents and sediments to solve global resource scarcities. The research aims to provide background on seabed exploitation and to apply a holistic systems engineering approach to develop general guidelines for sustainable seabed mining of polymetallic sulphides and a new concept and solutions for the Atlantis II Deep deposit in the Red Sea.The research methodology will start with acquiring a broader academic and industrial view on sustainable seabed mining through an online survey and expert interviews on seabed mining. In addition, the Nautilus Minerals case is reviewed for lessons learned and identification of challenges. Thereafter, a new concept for Atlantis II Deep is developed that based on a site specific assessment.The research undertaken in this study provides a new perspective regarding sustainable seabed mining. The main contributions of this research are the development of extensive guidelines for key issues in sustainable seabed mining as well as a new concept for seabed mining involving engineering systems, environmental risk mitigation, economic feasibility, logistics and legal aspects

Investigating the Predictability of a Chaotic Time-Series Data using Reservoir Computing, Deep-Learning and Machine- Learning on the Short-, Medium- and Long-Term Pricing of Bitcoin and Ethereum.

This study will investigate the predictability of a Chaotic time-series data using Reservoir computing (Echo State Network), Deep-Learning(LSTM) and Machine- Learning(Linear, Bayesian, ElasticNetCV , Random Forest, XGBoost Regression and a machine learning Neural Network) on the short (1-day out prediction), medium (5-day out prediction) and long-term (30-day out prediction) pricing of Bitcoin and Ethereum Using a range of machine learning tools, to perform feature selection by permutation importance to select technical indicators on the individual cryptocurrencies, to ensure the datasets are the best for predictions per cryptocurrency while reducing noise within the models. The predictability of these two chaotic time-series is then compared to evaluate the models to find the best fit model. The models are fine-tuned, with hyperparameters, design of the network within the LSTM and the reservoir size within the Echo State Network being adjusted to improve accuracy and speed. This research highlights the effect of the trends within the cryptocurrency and its effect on predictive models, these models will then be optimized with hyperparameter tuning, and be evaluated to compare the models across the two currencies. It is found that the datasets for each cryptocurrency are different, due to the different permutation importance, which does not affect the overall predictability of the models with the short and medium-term predictions having the same models being the top performers. This research confirms that the chaotic data although can have positive results for shortand medium-term prediction, for long-term prediction, technical analysis basedprediction is not sufficient

Performance Evaluation - Annual Report Year 3

This report describes the work done and results obtained in third year of the CATNETS project. Experiments carried out with the different configurations of the prototype are reported and simulation results are evaluated with the CATNETS metrics framework. The applicability of the Catallactic approach as market model for service and resource allocation in application layer networks is assessed based on the results and experience gained both from the prototype development and simulations. --Grid Computing

Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice - referred to as cashtag piggybacking - perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market

An Experimental Review on Deep Learning Architectures for Time Series Forecasting

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting; and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50000 time series divided into 12 different forecasting problems. By training more than 38000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient

Green of Another Color: Building Effective Relationships Between Foundations and the Environmental Justice Movement

The aim of this report is to help forge more effective partnerships between and within the environmental justice movement and the philanthropic community. In particular, the report should serve as an important educational tool for current and potential funders by: (1) providing information regarding the importance and accomplishments of the environmental justice movement over the last ten years, including those of the strategic networks; (2) demonstrating the gross underfunding the movement by the philanthropic community in general, and the Environmental Grantmakers Association membership in particular, in relation to other segments of the environmental movement; (3) providing recommendations as to which grantmaking practices would be most appropriate given the structure and needs of the movement, (4) discussing the importance of diversity and inclusive practices in foundation settings for improving environmental grantmaking practices and for overcoming the funding barriers currently confronting the environmental justice movement; and (5) evaluating the manner in which grantmakers can better utilize their institutional clout to support the work of the environmental justice movement beyond the disbursement of grants by undertaking mission-related investing strategies and mission-related shareholder actions against socially and ecologically irresponsible companies. We envision this document as being a valuable resource for foundation staff, officers, and board members, as well as individual donors and participants in the environmental justice movement