Global Data On Events, Location, And Tone Research Articles

Measuring news media sentiment using big data for Chinese stock markets

We construct and assess new time series measures of news media sentiment based on Global Data on Events, Location, and Tone (GDELT) using Data Science techniques. Five sentiment measures representing the news media Tone, Optimism, Attention, Tone Dispersion, and Emotional Polarity of Chinese stock markets are constructed based on article tone scores and media coverages from GDELT. All these news media sentiment measures are shown to have significant predictive power for Chinese stock market returns and volatilities. We also document substantial asymmetric sentiment effects on the Chinese stock market returns and volatilities. Sentiment extended EGARCH models are shown to improve market return and volatility forecasting accuracy significantly.

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.

Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

Social unrest events are common happenings in modern society which need to be proactively handled. An effective method is to continuously assess the risk of upcoming social unrest events and predict the likelihood of these events. Our previous work built a hidden Markov model- (HMM-) based framework to predict indicators associated with country instability, leaving two shortcomings which can be optimized: omitting event participants’ interaction and implicitly learning the state residence time. Inspired by this, we propose a new prediction framework in this paper, using frequent subgraph patterns and hidden semi-Markov models (HSMMs). The feature called BoEAG (Bag-of-Event-Association-subGraph) is constructed based on frequent subgraph mining and the bag of word model. The new framework leverages the large-scale digital history events captured from GDELT (Global Data on Events, Location, and Tone) to characterize the transitional process of the social unrest events’ evolutionary stages, uncovering the underlying event development mechanics and formulating the social unrest event prediction as a sequence classification problem based on Bayes decision. Experimental results with data from five main countries in Southeast Asia demonstrate the effectiveness of the new method, which outperforms the traditional HMM by 5.3% to 16.8% and the logistic regression by 11.2% to 43.6%.

The cooperative and conflictual interactions between the United States, Russia, and China: A quantitative analysis of event data

The United States, Russia and China are militarily and economically among the most powerful countries in the post-Cold War period, and the interactions between the three powers heavily influence the international system. However, different conclusions about this question are generally made by researchers through qualitative analysis, and it is necessary to objectively and quantitatively investigate their interactions. Monthly-aggregated event data from the Global Data on Events, Location and Tone (GDELT) to measure cooperative and conflictual interactions between the three powers, and the complementary cumulative distribution function (CCDF) and the vector autoregression (VAR) method are utilized to investigate their interactions in two periods: January, 1991 to September, 2001, and October, 2001 to December, 2016. The results of frequencies and strengths analysis showed that: the frequencies and strengths of USA-China interactions slightly exceeded those of USA-Russia interactions and became the dominant interactions in the second period. Although that cooperation prevailed in the three dyads in two periods, the conflictual interactions between the USA and Russia tended to be more intense in the second period, mainly related to the strategic contradiction between the USA and Russia, especially in Georgia, Ukraine and Syria. The results of CCDF indicated that similar probabilities in the cooperative behaviors between the three dyads, but the differences in the probabilities of conflictual behaviors in the USA-Russia dyad showed complicated characteristic, and those between Russia and China indicated that Russia had been consistently giving China a hard time in both periods when dealing with conflict. The USA was always an essential factor in affecting the interactions between Russia and China in both periods, but China’s behavior only played a limited role in influencing the interactions between the USA-Russia dyad. Our study provides quantitative insight into the direct cooperative and conflictual interactions between the three dyads since the end of the Cold War and helps to understand their interactions better.

Társadalmi mozgalmak a visegrádi országokban: konfliktus vagy együttműködés? Empirikus elemzés

Globalization and Europeanization have significantly contributed to a change in the role of the nation states. Considering the specifics of Central and Eastern European political culture, major societal changes are most likely to be triggered by social movements. Despite numerous researches, it is still debated whether the most significant social movements of the V4 countries tend to choose the cooperative or the conflictual way as their dominant action strategy. The purpose of this paper is to examine the general tendencies of the social movements using empirical data. The study seeks answers to the following questions: a) Which norms best characterize the “stable” social order of the V4 countries? b) What kind of actors would like to bring change?c) Is there a significant difference between the actors of the national, respectively, the EU action spheres and their dominant modes of action? The research uses data provided by the European Social Survey, GDELT (Global Data on Events, Location and Tone), and the Transparency Register of the European Union.

Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study

Tracking global and regional conflict zones requires spatially explicit information in near real-time. Here, we examined the potential of remote sensing time-series data (night lights) and big data (data mining of news events and Flickr photos) for monitoring and understanding crisis development and refugee flows. We used the recent Arab Spring as a case study, and examined temporal trends in monthly time series of variables which we hypothesized to indicate conflict intensity, covering all Arab countries. Both Flickr photos and night-time lights proved as sensitive indicators for loss of economic and human capital, and news items from the Global Data on Events, Location and Tone (GDELT) project on fight events were positively correlated with actual deaths from conflicts. We propose that big data and remote sensing datasets have potential to provide disaggregated and timely data on conflicts where official statistics are lacking, offering an effective approach for monitoring geopolitical and environmental changes on Earth.

Using four different online media sources to forecast the crude oil price

This study looks for signals of economic awareness on online social media and tests their significance in economic predictions. The study analyses, over a period of 2 years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter; Google Trends; Wikipedia; and the Global Data on Events, Location and Tone (GDELT) database. Semantic analysis is applied to study the sentiment, emotionality and complexity of the language used. Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) models are used to make predictions and to confirm the value of the study variables. Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting. Twitter language complexity, GDELT number of articles and Wikipedia page reads have the highest predictive power. This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens. In comparison with previous work, more media sources and more dimensions of the interaction and of the language used are combined in a joint analysis.

Predicting Social Unrest Events with Hidden Markov Models Using GDELT

Proactive handling of social unrest events which are common happenings in both democracies and authoritarian regimes requires that the risk of upcoming social unrest event is continuously assessed. Most existing approaches comparatively pay little attention to considering the event development stages. In this paper, we use autocoded events dataset GDELT (Global Data on Events, Location, and Tone) to build a Hidden Markov Models (HMMs) based framework to predict indicators associated with country instability. The framework utilizes the temporal burst patterns in GDELT event streams to uncover the underlying event development mechanics and formulates the social unrest event prediction as a sequence classification problem based on Bayes decision. Extensive experiments with data from five countries in Southeast Asia demonstrate the effectiveness of this framework, which outperforms the logistic regression method by 7% to 27% and the baseline method 34% to 62% for various countries.