Food Prices, Social Unrest and the Facebook Generation

Demand and Price Analysis, Food Consumption/Nutrition/Food Safety,

Let Them Tweet Cake: Estimating Public Dissent Using Twitter

This paper establishes a new method of estimating public dissent that is both cost-effective and adaptable. Twitter allows users to post short messages that can be viewed and shared by other users, creating a network of freely and easily observable information. Drawing data directly from Twitter, we collect tweets containing specified words and phrases from citizens voicing dissatisfaction with their government. The collected tweets are processed using a regular expression based algorithm to estimate individual dissent; which is aggregated to an overall measure of public dissent. A comparative case study of Canada and Kenya during the summer of 2016 provides proof of concept. Controlling for user base differences, we find there is more public dissent in Kenya than Canada. This obvious, but necessary, result suggests that our measure of public dissent is a better representation of each country’s internal dynamics than other more sporadic measures. As a robustness check, we test our estimates against real-world civil unrest events. Results show our estimates of public dissent are significantly predictive of civil unrest events days before they occur in both countries

Understanding peace through the world news

Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace

Quantitative risk assessment tools for the EU's Eastern and Southern neighbourhoods

Understanding and anticipating violent conflict and the breakdown of governance in the European Union (EU) neighbourhood is complex. However, it is of great value for academia and EU foreign policy. How can the EU know about, prepare for, and possibly help prevent governance breakdown and violent conflict in its neighbourhood? To answer this question, we propose innovative quantitative approaches to capture violent conflict and governance breakdown through survey-based and non-survey-based data at the sub-national level. We assess different theoretical approaches to explaining violent conflict and governance breakdown with a focus on social resilience. Moreover, we discuss numerous methodological tools including random forests, Bayesian methods, and change point analysis. The paper highlights the possibility of measuring and predicting violent conflict and governance breakdown in the EU neighbourhood at the sub-national level. We underline our arguments with initial empirical analyses. Further, we point to several research gaps such as the necessity to develop data collection efforts in order to build analyses and predictions on better data

Using System Dynamics to Model and Better Understand State Stability

The world can be complex and dangerous - the loss of state stability of countries is of increasing concern. Although every case is unique, there are important common processes. We have developed a system dynamics model of state stability based on an extensive review of the literature and debriefings of subject matter experts. We represent the nature and dynamics of the ‘loads’ generated by insurgency activities, on the one hand, and the core features of state resilience and its ‘capacity’ to withstand these ‘loads’, on the other. The challenge is to determine when threats to stability override the resilience of the state and, more important, to anticipate conditions under which small additional changes in anti-regime activity can generate major disruptions. With these insights, we can identify appropriate and actionable mitigation factors to decrease the likelihood of radical shifts in behavior and enhance prospects for stability

An online framework for civil unrest prediction using tweet stream based on tweet weight and event diffusion

Twitter is one of most popular Internet-based social networking platform to share feelings, views, and opinions. In recent years, many researchers have utilized the social dynamic property of posted messages or tweets to predict civil unrest in advance. However, existing frameworks fail to describe the low granularity level of tweets and how they work in offline mode. Moreover, most of them do not deal with cases where enough tweet information is not available. To overcome these limitations, this article proposes an online framework for analyzing tweet stream inpredicting future civil unrest events. The framework filters tweet stream and classifies tweets using linear Support Vector Machine (SVM) classifier. After that, the weight of the tweet is measured and distributed among extracted locations to update the overall weight in each location in a day in a fully online manner. The weight history is then used to predict the status of civil unrest in a location. The significant contributions of this article are (i) A new keyword dictionary with keyword score to quantify sentiment in extracting the low granularity level of knowledge (ii) A new diffusion model for extracting locations of interest and distributing the sentiment among the locations utilizing the concept of information diffusion and location graph to handle locations with insufficient information (iii) Estimating the probability of civil unrest and determining the stages of unrest in upcoming days. The performance of the proposed framework has been measured and compared with existing logistic regression based predictive framework. The results showed that the proposed framework outperformed the existing framework in terms of F1 score, accuracy, balanced accuracy, false acceptance rate, false rejection rate, and Matthews correlation coefficient