Strategic Use of Ad Hoc Commissions for Blame Avoidance: Evidence From Chile

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.

In a comparative way the article considers the practice of quantitative analysis of large textual data in Russian and foreign political science using the example of the GDELT (Global Database of Events, Language, and Tone) database. Based on the review of scientific publications by Russian and foreign researchers, the article shows a variety of research tasks that can be solved using GDELT data. At the same time, the tasks solved by national political scientists using GDELT data are mainly descriptive in nature, while the works of foreign scientists are characterized by the incorporation of GDELT data into complex mathematical models in order to determine the nature and direction of relationships between variables. It is revealed that the variability of research is due to the wide functionality of the GDELT database. The main advantages of using GDELT are, firstly, the possibility for automated event and content analysis of large textual data, secondly, an additional option for operationalizing and validating political phenomena and processes, and thirdly, the possibility of falsifying the results of the analysis. The limitations of working with the database were identified as, firstly, the imperfections of the CAMEO ontology, secondly, the incompleteness of the data contained in the database, and thirdly, the technical limitations of working with the GDELT database. Thereby, the author concluded that GDELT data should be used with extreme caution, and that measurements should be checked for validity in each case.

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.

The availability of large event data brings new opportunities for social analysis and informed policy-making processes. GDELT (the Global Database of Events, Language, and Tone) is a knowledge base that contains event data from news media from all around the world in different languages, and captures the main events about physical activities, entities and concepts. Semantically annotating and representing event data from GDELT for knowledge graph construction in real time is a challenging task. Current related work does not benefit from other freely available background information and Linked Open Data (LOD) sources. This paper presents ongoing work on automatic semantic annotation and representation of the GDELT 2.0 event stream in real time and discusses the benefits of representing events using semantic technologies according to an Event Description Ontology.

This study investigates the development and application of the GDELT (Global Database of Events, Language, and Tone) news database. Through experiments, we conducted a quantitative statistical analysis of the GDELT event database to evaluate its practical characteristics. The results indicate that although the database achieves comprehensive coverage across all countries and regions and includes most major global media outlets, the accuracy rate of its key fields is only approximately 55%, with a data redundancy as high as 20%. Based on these findings, while the GDELT data demonstrates good coverage and data integrity, data correction and deduplication are recommended before its use in research contexts and industrial applications. Subsequently, a survey of the existing literature reveals that current studies using GDELT primarily focused on event-related metrics, such as event quantity, tone, and GoldsteinScale, for application in international relations analysis, crisis event prediction, policy effectiveness testing, and public opinion impact analysis. Nevertheless, news constitutes a fundamental channel of information dissemination in media networks, and the propagation of news events through these networks represents a critical area of study for information recommendation, public opinion guidance, and crisis intervention. Existing research has employed the Event, GKG, and Mentions tables to construct cross-national news flow network models. However, the informational correlations across different data table fields have not been fully leveraged in preliminary data selection, leading to substantial computational overhead. To advance research in this field, this study employs chained list queries on the Event and Mentions tables within GDELT. Using social network analysis, we constructed a media co-occurrence network of event reports, through which core hubs and associative relationships within the event dissemination network are identified.

Macroeconomic forecasting through news, emotions and narrative

Cyberattacks that target critical national infrastructure, such as hospitals, pose a significant threat to the safety and wellbeing of individuals, as evidenced by incidents like the WannaCry worldwide ransomware attack. To better understand vulnerabilities within the healthcare sector and develop preventive measures, it is crucial to examine the evolving nature of cyberthreats and the types of attacks occurring. In this article, we describe a multimethod approach comprising social networks analysis, natural language processing, and machine learning, using data from GDELT (Global Database of Events, Language, and Tone), to identify the prevalence of attacks on hospitals while considering the type of attack and its date. Through this approach, meaningful patterns in the evolution of cyberattacks are revealed by analyzing the relationships between emerging cyberattacks mentioned in news reports. Findings show that the number of attacks from 2017 to 2023 increased substantially, with hospitals being more prone to critical attacks such as cyberterrorism/state actor-sponsored criminal activities, advanced persistent threats, and distributed denial of service. Mapping real-time data from diverse sources using a multimethod approach, such as the framework proposed in this article, can lead to better understanding of the threat landscape. This is a crucial step in determining necessary cyberdefenses and informing the development of policy interventions to ensure the cybersecurity of critical national infrastructure.

The untapped potential of mining news media events for understanding environmental change

The growing demand for affordable, reliable, domestically sourced, and low-carbon electricity is a matter of concern and it is driven by several causes including public policy priorities. Policy objectives and new technologies are changing wholesale market design. The analysis of different aspects of energy markets is increasingly on the agendas of academics, firms� managers or policy makers. Some concerns are global and are related to the evolution of climate change phenomena. Others are regional or national and they strongly appear in countries like Spain with a high dependence on foreign energy sources and high potential of domestic renewable energy sources. We can find a relevant case in Spanish solar energy policy. A series of regulatory reforms since 2010 reduce revenues to existing renewable power generators and they end up the previous system of support to new renewable generation. This policy change has altered the composition of the energy market affecting investment decisions. In this paper, we analyze the public opinion about energy policy of the Spanish Government using the Global Database of Events, Language, and Tone (GDELT). The GDELT Project consists of over a quarter-billion event records in over 300 categories covering the entire world from 1979 to present, along with a massive network diagram connecting every person, organization, location, and theme to this event database. Our aim is to build sentiment indicators arising from this source of information and, in a final step, evaluate if positive and negative indexes have any effect on the evolution of key market variables as prices and demand.

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.

For many citizens, the main source of information about world events is the news media. Undeniably, media coverage of topics influences the perceived importance of these topics. Recent media discourse has given extensive attention to the topic of refugees and migration. In this context, we conduct a study on news media coverage of refugees for the time period of January 1st 2016 to December 31st 2016. We rely on the Global Database of Events, Language, and Tone (GDELT) project that monitors and analyses news articles around the world. In a first phase, we explore the global media conversation on refugees along two important dimensions: article quantity and sentiment. We identify and characterize events that generated an extensive media coverage and also extreme sentiment in the news reports. In a second phase, we refine the analysis by focusing on the news media coverage related to refugees in Europe. GDELT features allow us to identify the key countries and time evolution of the media coverage. Lastly, we determine the main actors linked to refugees in the news and study their interaction through a network analysis.

Following Paul Pierson’s work on the New Politics of the welfare state, numerous studies on welfare state reforms have shown that governments enacting welfare cuts regularly employ blame avoidance strategies and use issue frames when they communicate welfare reform policies. However, it remains largely unexplained to what extent these blame avoidance strategies really impact on the attitudes of voters on the micro level. This study sets out to fill that void in the literature. Using data on pension reforms and student grant cutbacks, the article provides experimental evidence showing that blame avoidance and framing strategies affect individual attitudes towards the proposed policies – in particular in the case of pension reforms. Moreover, in the case of pensions, the impact is conditioned by individual risk exposure. These results add significantly to the literature on blame avoidance and welfare state reform policies by indicating that successful blame avoidance may be the reason why governments are not always punished for cutbacks to the welfare state.

This research aims to develop a novel framework that uncovers the causal influence of global events on public sentiment through temporal graph modeling and neural causal inference. Global events, such as pandemics, elections, and economic crises, profoundly affect public sentiments, shaping social behaviors and economic outcomes. Traditional models often fall short in capturing the complex, dynamic, and non-linear relationships between these events and sentiments. This paper presents the Neural Temporal Causal Graph Network (NTCGN), a unified framework that integrates temporal graph neural networks with a Causal Attention Network (CAN) to model and interpret these relationships. NTCGN constructs a temporal graph from event data and sentiment-labeled texts, learning dependencies and causal influences through advanced neural architectures. A thorough comparative analysis with state-of-the-art models such as Logistic Regression, SVM, LSTM, and transformer-based models demonstrates NTCGN’s superior performance. Experimental evaluation using the Sentiment140 and Global Database of Events, Language and Tone (GDELT) 2.0 datasets shows NTCGN achieving an accuracy of 0.798 and an F1 score of 0.795, outperforming these baseline models. The model’s causal inference capabilities are validated using the Causal Impact Score (CIS) and Causal Discovery Precision (CDP), highlighting its reliability in identifying true causal links. Visualizations of attention maps and causal pathways enhance interpretability, demonstrating how specific events influence public sentiments. This work provides a robust and interpretable tool for analyzing event-driven sentiment dynamics in real-world applications.

Nationalistic political rhetoric: measurement and preliminary insights

The travesty of the Life Esidimeni project in South Africa, which claimed the lives of 144 mental health users at psychiatric facilities in Gauteng between 2016 and 2017, is multifaceted. One facet involves strategies of blame avoidance designed to escape liability for the deaths that were expressed by the former Member of the Executive Council for Health and other public health officials during the Life Esidimeni Arbitration Hearings. These hearings were broadcast on state television between October 2017 and 2018, and eight samples from the hearings were analysed for specific blame avoidance strategies. Following the principles of qualitative discourse analysis, this paper extends research on blame avoidance behaviour in the public administration and policy domain, exploring three key officials’ micro- and macro-level choices of blame avoidance in the context of the arbitration hearings to develop a more comprehensive account of these strategies. A public hearing is a discourse setting that is reactionary in nature, and our findings on the micro-level, a neglected dimension of research on blame avoidance behaviour, extend our understanding of these behaviours. We propose that at least two continua for blame avoidance are relevant in this setting.