Global Database Of Events, Language, And Tone Research Articles

Evaluating the bulk commodities supply risk from the perspective of physical trade

Geopolitical conflicts, trade barriers, and other factors pose great risks to the stable supply of bulk commodities; however, cross-country and inter-temporal assessments and comparisons of various bulk commodity supply risks from the physical trade perspective are still lacking. This gap hinders a holistic understanding of global resource security and further appropriate policy formulation. Based on the Net Positive Import share, the Worldwide Governance Indicator (WGI), and the Bilateral Relationship Indicator which was innovatively quantified using the Global Database of Events, Language, and Tone (GDELT) this study evaluated 32 bulk commodity supply risks (including agricultural products, metals, and energy fuels) in China, Japan, and the USA for the period 1992–2019. We found that the three countries have experienced different supply risk evolutions related to their diverse domestic resource endowments and socioeconomic developmental stages. During the study period, China had increasing supply risks for 20 of the 32 bulk commodities compared to 10 for Japan and the USA. The overall bulk commodity supply risk increased substantially for China, remained high for Japan, and remained relatively low for the USA. Under increasing global trade system uncertainty, in addition to the supply diversification strategy, we advise net importer countries to utilise news media data such as GDELT to monitor and analyse geopolitical changes, especially for major suppliers, which would help improve resilience to supply disruptions.

Transboundary cooperation in Arctic climate change governance under geopolitical tensions

Political conflicts or geopolitical tensions can create uncertainty in addressing climate change and environmental management in the Arctic. Dissecting how actors interact with each other and form networks is important for understanding ecological and environmental management challenges during geopolitical tensions, as well as promoting better governance. We construct transboundary networks for Arctic climate change governance (ACCG) from 2013 to 2021 based on the Global Database of Events, Language, and Tone (GDELT). Further, we used network descriptive statistical analysis and Temporal Exponential Random Graph Models (TERGM) to explore the structure of ACCG networks and the key factors influencing cooperation formation. The findings suggest that the overall cooperation density of the ACCG is low, and the dominant position of core actors is continuously strengthening. Non-state actors are less likely to be seen as partners and their participation depends largely on cooperation with states. The results also show that actors with similar stances and problem exposure are more likely to cooperate, but those exposed to high latitudes often choose not to cooperate; first-comers are more likely to perceive as cooperating yet they are inclined to establish internal cooperation. Additionally, two geographically proximate actors are more likely to cooperate. This indicates that under geopolitical tensions, the ACCG faces challenges not only due to the limited capacity of non-state actors to perform transboundary functions but also because the cooperation mechanisms are influenced by regional political logic. Accordingly, we further suggest policy recommendations from developing binding international frameworks to guide transboundary cooperation, enhancing cooperation among non-state actors, and ensuring the representativeness and fairness of non-Arctic actors' participation. This research provides insights into transboundary environmental management under political tensions, while also offering new pathways for analysing large-scale environmental governance structures.

Nationalistic political rhetoric: measurement and preliminary insights

While nationalistic political rhetoric has become a critical concern for international business, the research in this area has lagged because of the unavailability of a representative measure. In this study, I introduce a largely unexplored source of data for international business research: Global Database of Events, Language, and Tone (GDELT), and explain how GDELT can be used to operationalize politicians' use of nationalistic rhetoric across the world. After discussing the data and the operationalization of the measure, I offer some preliminary insights. They reveal that the rise of nationalistic political rhetoric is not a recent phenomenon but a consistent theme over the last three decades. Contrary to suggestions in the literature, it is not low-income or less developed environments but countries with higher income and well-developed institutions where nationalistic rhetoric is more visible and where it strongly deters FDI. The measure advanced in this study offers a renewed understanding of global sociopolitical risks. It also contributes to institutional theory by explicating how politicians use non-bureaucratic influence to affect informal institutions.

Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet Mentions

Social media platforms are valuable data sources in the study of public reactions to events such as natural disasters and epidemics. This research assesses for selected countries around the globe the time lag between daily reports of COVID-19 cases and GDELT (Global Database of Events, Language, and Tone) and Twitter (X) COVID-19 mentions between February 2020 and April 2021 using time series analysis. Results show that GDELT articles and tweets preceded COVID-19 infections in Australia, Brazil, France, Greece, India, Italy, the U.S., Canada, Germany, and the U.K., while for Poland and the Philippines, tweets preceded and GDELT articles lagged behind COVID-19 disease incidences, respectively. This shows that the application of social media and news data for surveillance and management of pandemics needs to be assessed on a case-by-case basis for different countries. It also points towards the applicability of time series data analysis for only a limited number of countries due to strict data requirements (e.g., stationarity). A deviation from generally observed lag patterns in a country, i.e., periods with low COVID-19 infections but unusually high numbers of COVID-19-related GDELT articles or tweets, signals an anomaly. We use the seasonal hybrid extreme Studentized deviate test to detect such anomalies. This is followed by text analysis of news headlines from NewsBank and Google on the date of these anomalies to determine the probable event causing an anomaly, which includes elections, holidays, and protests.

Exploring the challenges of the COVID-19 vaccine supply chain using social media analytics: A global perspective

The Coronavirus (COVID-19) pandemic has highlighted the importance of effective vaccine supply chain management (SCM). As the world raced to curb the spread of the virus, the efficient distribution of vaccines became crucial to ending the pandemic. However, the challenges of distributing COVID-19 vaccines globally were exacerbated by the pandemic itself, leading to delays, shortages, and disruptions under disaster conditions. In this study, we use social media analytics to explore the impact of COVID-19 vaccine supply chain challenges from a global perspective. We collected data from two social media platforms—Twitter and the Global Database of Events, Language and Tone (GDELT)—between January 2020 and November 2022. The analysis is developed leveraging Natural Language Processing (NLP) techniques, such as topic modelling and sentiment analysis. We found that vaccine supply chain challenges significantly impacted the ability of healthcare systems to respond to the pandemic, with significant differences between regions (such as in the case of developing countries). Our findings highlight the need for improved coordination and collaboration in vaccine SCM to ensure equitable access. In addition, this research provides valuable practical insight into using social media analytics and its potential to enhance decision-making capabilities for policymakers and industry leaders.

Evolutionary dynamics of the city brand influence of top ten global cities: Characteristics analysis driven by global news big data and intelligent semantic mining

This study focuses on the long-term comparison of the city brand influence constructed and reflected by global news media. Specifically, from the analytical perspective of two paths and five characteristics, this study empirically and quantitatively assesses and deconstructs the evolutionary dynamics of the city brand influence of the top ten Globalization and the World Research Group (GaWC) Alpha+ or above global cities over five consecutive years based on intelligent semantic embedding mining on the Global Database of Events, Language, and Tone (GDELT) full-sample global news big data, along with statistical analysis and machine learning. The results indicate that the top ten global cities' city brand influence over five consecutive years fluctuated from high to low and then rebounded. The hierarchical tiers of the Alpha++ and Alpha+ global cities' city brand influence were relatively stable. However, there are inner tensions in the evolutionary trend, such as the uneven rank differences between the city brand influence soft power and the hard power of these cities, and the intersection of clustering categories between the Alpha++ and Alpha+ global cities. The findings and data of this study provide concrete references and guidelines for policymaking toward future competition of top global cities' city brand influence.

How are Africans talking about climate change and who is doing the talking?

This study identifies who is talking about climate change in Africa, both in the mainstream media and on Twitter, and analyses the key messages emerging from the different platforms. For the mainstream media, we used Google’s Global Database of Events, Language and Tone (GDELT) platform to access articles using the search terms ‘climate change AND Africa’ or ‘climate change’ and the name of all 54 African countries. We then identified the top five countries with the most articles in the sample and using random sampling, undertook a frame analysis of the articles. Regarding Twitter, we downloaded tweets containing ‘climate change AND Africa’ or ‘climate change’ and the name of all 54 African countries, identified who was tweeting and what they were tweeting about. We also identified key African climate change activists and analysed their tweets. While the nature of mainstream media coverage varies across the top five countries, a slight shift towards articles focused on adaptation and mitigation was observed, away from purely disaster narratives. Worryingly, for Twitter, very few African voices are tweeting about climate change and what they are tweeting does not draw much attention to pertinent issues on the continent in respect of climate change.

Plant pest invasions, as seen through news and social media

Invasion by exotic pests into new geographic areas can cause major disturbances in forest and agricultural systems. Early response can greatly improve containment efforts, underscoring the importance of collecting up-to-date information about the locations where pest species are being observed. However, existing invasive species databases have limitations in both extent and rapidity. The spatial extent is limited by costs and there are delays between species establishment, official recording, and consolidation. Local online news outlets have the potential to provide supplemental spatial coverage worldwide and social media has the potential to provide direct observations and denser historical data for modeling. Gathering data from these online sources presents its own challenges and their potential contribution to historical tracking of pest invasions has not previously been tested. To this end, we examine the practical considerations for using three online aggregators, the Global Database of Events, Language and Tone (GDELT), Google News, and a commercial media listening platform, Brandwatch, to support pest biosurveillance. Using these tools, we investigate the presence and nature of cogent mentions of invasive species in these sources by conducting case studies of online news and Twitter excerpts regarding two invasive plant pests, Spotted Lanternfly and Tuta absoluta. Our results using past data demonstrate that online news and social media may provide valuable data streams to supplement official sources describing pest invasions.

COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies.

An excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of hospitalizations for COVID-19 by considering age at admission as a potential explanatory factor. Age at hospitalization provides a clear idea of the epidemiological impact of the disease, as the elderly population is more at risk of severe COVID-19 outcomes. Administrative data for the Veneto region, Northern Italy from February 1, 2020, to November 20, 2021, were considered. An inferential approach based on quasi-likelihood estimates through the generalized estimation equation (GEE) Poisson link function was used to quantify the overdispersion. The daily variation in the number of hospitalizations in the Veneto region that lagged at 3, 7, 10, and 15 days was associated with the number of news items retrieved from Global Database of Events, Language, and Tone (GDELT) regarding containment interventions to determine whether the magnitude of the past variation in daily hospitalizations could impact the number of preventive policies. This study demonstrated a significant increase in the pattern of hospitalizations for COVID-19 in Veneto beginning in December 2020. Age at admission affected the excess variability in the number of admissions. This effect increased as age increased. Specifically, the dispersion was significantly lower in people under 30 years of age. From an epidemiological point of view, controlling the overdispersion of hospitalizations and the variables characterizing this phenomenon is crucial. In this context, the policies should prevent the spread of the virus in particular in the elderly, as the uncontrolled diffusion in this age group would result in an extra variability in daily hospitalizations. This study demonstrated that the overdispersion, together with the increase in hospitalizations, results in a lagged inflation of the containment policies. However, all these interventions represent strategies designed to contain a mechanism that has already been triggered. Further efforts should be directed toward preventive policies aimed at protecting the most fragile subjects, such as the elderly. Therefore, it is essential to implement containment strategies before the occurrence of potentially out-of-control situations, resulting in congestion in hospitals and health services.

Lifting the Veil on the Use of Big Data News Repositories: A Documentation and Critical Discussion of A Protest Event Analysis

ABSTRACT This paper presents a critical discussion of the processing, reliability and implications of free big data repositories. We argue that big data is not only the starting point of scientific analyses but also the outcome of a long string of invisible or semi-visible tasks, often masked by the fetish of size that supposedly lends validity to big data. We unpack these notions by illustrating the process of extracting protest event data from the Global Database of Events, Language and Tone (GDELT) in six European countries over a period of seven years. To stand up to rigorous scientific scrutiny, we collected additional data by computational means and undertook large-scale neural-network translation tasks, dictionary-based content analyses, machine-learning classification tasks, and human coding. In a documentation and critical discussion of this process, we render visible opaque procedures that inevitably shape any dataset and show how this type of freely available datasets require significant additional resources of knowledge, labor, money, and computational power. We conclude that while these processes can ultimately yield more valid datasets, the supposedly free and ready-to-use big news data repositories should not be taken at face value.

Adopting big data to create an “outside-in” global perspective of guanxi

Previous research on the Chinese notion of guanxi has tended to use descriptive approaches to study its prevalence and influence on business and Chinese consumers. Relatively less research has focused on perceptions of guanxi in other parts of the world. This paper addresses this important research gap through adopting an “outside-in” global perspective using big data. In particular, the study draws on 162 million guanxi-related news articles during 2017–2020 extracted from the Global Database of Events, Language and Tone (GDELT), an open-source, real-time current affairs repository of online news and event metadata. The findings reveal that guanxi is heavily influenced by geopolitical and public health issues. The study also discovered a major contrast in the overall tone between China, being slightly positive, and the US and Germany, being largely negative, with the association varying according to changes in the marketing ecosystem.

Sino-U.S. Relations and the Demand for Military Expenditure in the Indo-Pacific Region

ABSTRACT This paper examines the demand for military expenditure in eighteen selected Indo-Pacific countries for the years 1993–2018. As the dominant powers, the U.S. and China characterize the geopolitical structure of the Indo-Pacific region. Sino-U.S. relations are newly quantified by measuring the number of cooperative and conflict events between China and the United States based on the Global Database of Events, Language, and Tone (GDELT). After incorporating Sino-U.S. relation variables into neoclassical demand models, the panel data estimating results reveal that the increasing number of confrontations from the United States toward China has lead to increases in non-U.S. allies’ military expenditure while the rise in China confronting the United States has not. U.S. allies in the Indo-Pacific region have tended to increase military expenditure when the United States increases its pressure on China. The empirical results provide evidence that Sino-U.S. relations affect the level of military expenditure in the Indo-Pacific Region.

Big Data-Based Assessment of Political Risk along the Belt and Road

Political risk assessment has become increasingly important in recent years, especially with the launch of the Belt and Road Initiative (BRI) and with Covid-19 still ravaging the world. This study aims to assess systematically the political risk of BRI countries during the period from 2013 to 2019 based on three big data sets, the Global Database of Events, Language, and Tone (GDELT), China Global Investment Tracker (CGIT), and Armed Conflict Location & Event Data Project (ACLED). It is found that to properly quantify the political risks for BRI countries, the type of events, “Material Conflict”, and a variable characterizing the degree of cooperation/conflicts of the events, the Goldstein Scale, are of critical importance. Based on the chosen type of events and variable, we design a normalized variable to assess political risk of any country in any year so that comparison among different countries can be meaningly made. By decomposing political risk into two components, domestic and international, and examining the spatiotemporal evolution of political risk along the Belt and Road, we find that the sum of the number of BRI countries with the extremely high level and the high level of domestic, international, and (overall) political risk all reached the peak in 2015, and decreased thereafter, and that often the level of domestic political risk along the Belt and Road was higher than the international political risk. It is also found that a strong positive correlation exists between political risk and China’s total investments and construction contracts along the Belt and Road during this period. The implications of this positive correlation are discussed. The analysis presented here may help to promote the sustainable development of BRI, and be extended to examine the risks associated with foreign investments other than BRI projects.

Macroeconomic forecasting through news, emotions and narrative

This study proposes a new method of incorporating emotions from newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. For the most part, existing research includes positive and negative tone only to improve macroeconomic forecasts, focusing predominantly on large economies such as the US. These works use mainly anglophone sources of narrative, thus not capturing the entire complexity of the multitude of emotions contained in global news articles. This study expands the existing body of research by incorporating a wide array of emotions from newspapers around the world – extracted from the Global Database of Events, Language and Tone (GDELT) – into macroeconomic forecasts. We present a thematic data filtering methodology based on a bi-directional long short term memory neural network (Bi-LSTM) for extracting emotion scores from GDELT and demonstrate its effectiveness by comparing results for filtered and unfiltered data. We model industrial production and consumer prices across a diverse range of economies using an autoregressive framework, and find that including emotions from global newspapers significantly improves forecasts compared to three autoregressive benchmark models. We complement our forecasts with an interpretability analysis on distinct groups of emotions and find that emotions associated with happiness and anger have the strongest predictive power for the variables we predict.

Comparisons of the City Brand Influence of Global Cities: Word-Embedding Based Semantic Mining and Clustering Analysis on the Big Data of GDELT Global News Knowledge Graph

Global cities act as influential hubs in the networked world. Their city brands, which are projected by the global news media, are becoming sustainable resources in various global competitions and cooperations. This study adopts the research paradigm of computational social science to assess and compare the city brand attention, positivity, and influence of ten Globalization and World Cities Research Network (GaWC) Alpha+ global cities, along with their dimensional structures, based on combining the cognitive and affective theoretical perspectives on the frameworks of the Anholt global city brand dimension system, the big data of global news knowledge graph in Google’s Global Database of Events, Language, and Tone (GDELT), and the technologies of word-embedding semantic mining and clustering analysis. The empirical results show that the overall values and dimensional structures of city brand influence of global cities form distinct levels and clusters, respectively. Although global cities share a common structural characteristic of city brand influence of the dimensions of presence and potential being most prominent, Western and Eastern global cities differentiate in the clustering of dimensional structures of city brand attention, positivity, and influence. City brand attention is more important than city brand positivity in improving the city brand influence of global cities. The preferences of the global news media over global city brands fits the nature of global cities.

Home Field Advantage? EU-ACP Economic Partnership Agreement Meeting Locations and Textual Tone

The European Union’s Economic Partnership Agreements (EPAs) with countries in the African, Caribbean and Pacific (ACP) group are touted as a new age of equitable partnership between European countries and former colonies. However, many critics argue that the EPAs simply substitute a different form of political and economic domination. In this paper, we consider if the siting of meetings has a substantive impact on the language of media describing the EPAs and/or communiques and statements that arise from those discussions. Using data culled from the Global Database of Events, Language and Tone (GDELT) via Google’s BigQuery, we use a difference-in-difference like approach to evaluate if the tone and polarity of media reports about the EPAs during periods of ‘home’ meetings in the ACP countries differs from media reports during ‘away’ meetings in the EU. We supplement these findings with analysis of joint statements released from a subset of EPA meetings. In general, we find that ACP countries’ tone and interests are better represented in media reports during ‘home’ meetings, but this may not translate to any meaningful difference in actual meeting outcomes. As such, meeting siting may be only a limited tool for reducing political domination.

Online Monitoring of Global Attitudes Towards Wildlife

Human factors are increasingly recognised as central to conservation of biodiversity. Despite this, there are no existing systematic efforts to monitor global trends in perceptions of wildlife. With traditional news reporting now largely online, the internet presents a powerful means to monitor global attitudes towards species. In this work we develop a method using the Global Database of Events, Language, and Tone (GDELT) to scan global news media, allowing us to identify and download conservation-related articles. Applying supervised machine learning techniques, we filter irrelevant articles to create a continually updated global dataset of news coverage for seven target taxa: lion, tiger, saiga, rhinoceros, pangolins, elephants, and orchids, and observe that over two-thirds of articles matching a simple keyword search were irrelevant. We examine coverage of each taxa in different regions, and find that elephants, rhinos, tigers, and lions receive the most coverage, with daily peaks of around 200 articles. Mean sentiment was positive for all taxa, except saiga for which it was neutral. Coverage was broadly distributed, with articles from 73 countries across all continents. Elephants and tigers received coverage in the most countries overall, whilst orchids and saiga were mentioned in the smallest number of countries. We further find that sentiment towards charismatic megafauna is most positive in non-range countries, with the opposite being true for pangolins and orchids. Despite promising results, there remain substantial obstacles to achieving globally representative results. Disparities in internet access between low and high income countries and users is a major source of bias, with the need to focus on a diversity of data sources and languages, presenting sizable technical challenges. Tackling these will depend on the development of more advanced natural language tools in a wildlife conservation context, as well as the development of systems to access more, and more diverse, data sources.

A Domain Expertise and Word-Embedding Geometric Projection Based Semantic Mining Framework for Measuring the Soft Power of Social Entities

Social science research (communication studies in particular) often involves measuring the soft power of social entities from the related big data of media texts. This work proposes a soft power measurement framework that integrates the domain-expertise-driven conceptualization of the traditional social science paradigm and the data-driven operationalization of the computational social science paradigm. Based on the geometric projections of word vectors from the social entity keywords to the soft power dimension keywords, the weights of social entity keywords in the full-sample media texts are projected to the corresponding soft power dimensions in the high-dimensional word-embedding semantic space and accumulated to the total and dimensional values of the soft power of social entities respectively. We demonstrate the applicability, compatibility, computational complexity, validity, adaptability, and scalability of the proposed framework by showing a standard instantiation (measuring the national image carrying capacity of Chinese elite private entrepreneurs from the full-sample WeChat articles, each of which has more than 100,000 unique views) and a variational instantiation (measuring the city brand influence of global cities from the big data of the Global News Knowledge Graph in the Global Database of Events, Language, and Tone (GDELT) of Google) of the proposed framework.

Rising Powers and Grassroots Image Management: Confucius Institutes and China in the Media

Abstract This article proposes and tests a mechanism of grassroots image management to explain how rising powers craft an international environment more conducive to their interests. The aim is to promote the state’s foreign policy goals by influencing the perceptions of ordinary foreign citizens. To test this mechanism, we examine the impact of China’s Confucius Institutes (CIs) as an observable instrument of China’s grassroots image management strategy. Using data from the Global Database of Events, Language, and Tone (GDELT), we employ a spatial–temporal approach which finds that proximity to an active CI significantly and substantively improves the tone of media reporting about events relevant to China in that locality. The finding is robust to different specifications and estimation strategies, and is qualitatively consistent with results generated using household opinion data from Afrobarometer surveys. Theoretically, our results suggest the importance of systematically examining presentations and perceptions about rising powers at the popular level, in addition to focusing on elite attitudes, to understand discursive change. More directly, our findings reveal that CIs are helping to improve how China is viewed among foreign publics.

Spatial Big Data Analysis of Political Risks along the Belt and Road

As many regions along the Belt and Road have long been struggling with terrorist attacks, crimes, wars, and corruption, political risks pose important challenges for infrastructure projects and transnational investment. The objective of the article is to contribute to the identification of different types of political risks along the Silk Road Economic Belt and 21st Century Maritime Silk Road, and the visualization of their micro-level spatial distribution based on the Global Database of Events, Language, and Tone (GDELT) datasets from October 2013 to May 2018. By adopting the bivariate Moran’s I model to compare the distribution of political risks along the Belt and Road and that of the Chinese Belt and Road Initiative (BRI) investment and construction projects based on data from the China Global Investment Tracker (CGIT), the article also generates an overall political risk profile for Chinese BRI projects. Our findings show that a particularly high percentage of Chinese BRI projects are distributed in regions with high political risks. This research has important implications for the discussion and study of the BRI. First, by combining geographic spatial statistical analysis and political science conceptual frameworks, we point out the necessity to query the BRI from interdisciplinary perspectives grounded in empirical research. Second, the research delivers to researchers, academics, practitioners, consultants and policy makers interested in the BRI the latest insights into the risks and challenges along the Belt and Road. Third, it advocates policies and strategies conducive to identifying, assessing and mitigating political risks in investment along the Belt and Road and to strengthening the sustainable development of the BRI.