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The purpose of this paper is to analyze the market response of the aerospace and defense industry and the airline industry to the ongoing conflict between Ukraine and Russia based on the sentiments from war-related news articles over the period from October 2021 to June 2022.

The study uses the news article database of Global Database of Events, Languages and Tone (GDELT) to create a new set of variables that reflect the news sentiment regarding war and conflict. By investigating the newly created sentiment variables in combination with traditional event study methodology, the authors seek to find out whether sentiment indicators can be helpful to rationalize the evolution of the different stock markets before and after the conflict.

The authors' results point out a significant negative impact of the war on the airline market and a positive impact on the defense market. The authors' study also introduces a new set of war-related news-based sentiment variables that is significant to explain the evolution of the two markets before and after the war. The relationships between this study's new set of variables and the performance of the two markets are also proven to be significantly impacted by the invasion.

To the best of the authors' knowledge, this is the first research to use the news sentiment related to the topic of war and conflict to explain the market movement of different industries during the Ukraine invasion.

The purpose of this paper is to present a methodology to reveal complex events structures in events’ occurrences by analyzing event databases, targeting to systematizing events’ analysis and surpassing the need for idiographic approaches.

A process-oriented point of view is enabled by purposeful data transformations, and higher-order dependencies are discovered and exploited to capture the flows among the events.

Political events do not follow a linear movement that is implied by a sequence, but they occur in varying patterns that cannot be reflected accurately when assuming only first-order dependencies.

The methodology suggests a novel way to look and to analyze raw event data and it offers an accessible, practicable and supplementary tool as it does not disturb any of the established relevant research designs, and it does not require any additional data to be applied.

The author attempts to examine the existence and pattern of coalitions in international relations across countries, and investigates whether international relations of coalition partners influence a country's enaction of agricultural non-tariff measures (NTMs).

The author adopts a machine learning technique to identify international relation coalition partnerships and use network analysis to characterize the clustering pattern of coalitions with high-frequent records of global event data. The author then constructs a monthly dataset of agricultural NTMs against China and international relations with China of each importer and its coalition partners, and designs a panel structural vector autoregressive (PSVAR) model to estimate impulse response functions of agricultural NTMs with regard to international relation shocks.

The author finds countries to establish coalition partnerships. Two major clusters of coalitions are noted, with one composed of coalitions primarily among “North” countries and the other of coalitions among “South” countries. The United States is found to play a pivotal role by connecting the two clusters. The PSVAR estimation reveals reductions of NTMs against China following improved international relations with China of both the importer and its coalition partners. NTM responses are more substantial for measures that are trade restrictive. These results confirm that coalitions in international relations lead to coordination of agricultural NTMs.

The author provides international political insights into agricultural trade policymaking by showing interactions of NTM enaction across countries in the same coalition of international relations. These insights offer useful policy implications to predict and cope with hidden barriers to agricultural trade.

This paper discusses data-collection strategies that use digitized historical newspaper archives to study social conflicts and social movements from a global and historical perspective focusing on nationalist movements. I present an analysis of State-Seeking Nationalist Movements (SSNMs) dataset I, which includes news articles reporting on state-seeking activities throughout the world from 1804 to 2013 using the New York Times and the Guardian/Observer. In discussing this new source of data and its relative value, I explain the various benefits and challenges involved with using digitized historical newspaper archives for world-historical analysis of social movements. I also introduce strategies that can be used to detect and minimize some potential sources of bias. I demonstrate the utility of the strategies introduced in this paper by assessing the reliability of the SSNM dataset I and by comparing it to alternative datasets. The analysis presented in the paper also compares the labor-intensive manual data-coding strategies to automated approaches. In doing so, it explains why labor-intensive manual coding strategies will continue to be an invaluable tool for world-historical sociologists in a world of big data.

Facebook “likes” are often used as a proxy of users’ attention and an affirmation of what is posted on Facebook (Gerodimos & Justinussen, 2015). To determine what factors predict “likes,” the authors analyzed Facebook posts made by the campaigns of Hillary Clinton, Bernie Sanders, and Donald Trump, the top three candidates from the 2016 US primary election. Several possible factors were considered, such as the types of posts, the use of pronouns and emotions, the inclusion of slogans and hashtags, references made to opponents, as well as candidate’s mentions on national television. The results of an ordinary least-squared regression analysis showed that the use of highly charged (positive or negative) emotions and personalized posts (first-person singular pronouns) increased “likes” across all three candidates’ Facebook pages, whereas visual posts (posts containing either videos or photos) and the use of past tenses were liked more often by Hillary Clinton and Bernie Sanders’ followers than by Trump’s followers. Television mentions boosted likes on Clinton and Sanders’ posts but had a negative effect on Trump’s. The study contributes to the growing literature on digitally networked participation (Theocharis, 2015) and supports the emerging notion of the new “hybrid media” system (Chadwick, 2013) for political communication. The study also raises questions as to the relevance of platforms such as Facebook to deliberative democratic processes since Facebook users are not necessarily engaging with the content in an organic way, but instead might be guided to specific content by the Facebook timeline algorithm and targeted ads.

Social movement scholars use protest events as a way to quantify social movements and have most often used large, national newspapers to identify those events. This has introduced known and unknown biases into our measurement of social movements. We know that national newspapers tend to cover larger and more contentious events and organizations. Protest events are furthermore a small part of what social movements actually do. Without other readily available options to quantify social movements, however, big-N studies have continued to focus on protest events via a few large newspapers. With advances in digitized data and computational methods, we now no longer have to rely on large newspapers or focus only on protests to quantify important aspects of social movements. In this paper, we use the environmental movement as a case study, analyzing data from a wide range of local, regional, and national newspapers in the United States to quantify multiple facets of social movements. We argue that the incorporation of more data and new methods to quantify information in text has the potential to transform the way we both conceive of and measure social movements in three ways: (1) the type of focal social movement organization included, (2) the type of tactics and issues covered, and (3) the ability to go beyond protest events as the primary unit of analysis. In addition to demonstrating ways that the focus on counting protest events has introduced specific biases in the type of tactics, issues, and organizations covered in social movement research, we argue that computational methods can help us extract and count meaningful aspects of social movements well beyond event counts. In short, the infusion of new data and methods into social movements, peace, and conflict studies could lead us to a substantial shift in the way we quantify social movements, from protest events to everything that occurs outside of them.

The purpose of this paper is to propose an effective knowledge elicitation method and representation scheme that empowers humanitarian assistance/disaster relief (HA/DR) analysts and experts to create analytic models without the aid of data scientists and methodologists while addressing the issues of complexity, collaboration, and emerging technology across a diverse global network of HA/DR organizations.

The paper used a mixed-methods research approach, with qualitative research and analysis to select the model elicitation method, followed by quantitative data collection and evaluation to test the representation scheme. A simplified analytic modeling approach was created based on emerging activity-based intelligence (ABI) analytic methods.

Using open source data on the Syrian humanitarian crisis as the reference mission, ABI analytic models were proven capable in modeling HA/DR scenarios of physical systems, nonphysical systems, and thinking.

As a data-agnostic approach to develop object and network knowledge, ABI aligns with the objectives of modeling within multiple HA/DR organizations.

Using an analytic method as the basis for model creation allows for immediate adoption by analysts and removes the need for data scientists and methodologists in the elicitation phase. Applying this highly effective cross-domain ABI data fusion technique should also supplant the accuracy weaknesses created by traditional simplified analytic models.

This paper aims to discuss the ways in which multinational enterprises (MNEs) may contribute to growing inequality.

By showing some macroscopic evidence of business-related human rights infringements, this paper claims that the negative impacts of MNEs has been largely overlooked.

The extent to which MNEs contribute to income-based inequality through the abnormal accumulation of wealth by chief executive officers (CEOs), top managers and shareholders is known, and almost nothing is known about whether this is connected to MNEs’ track record of human rights infringements.

This paper suggest that there might be a connection between MNEs’ human rights infringements and the abnormal accumulation of wealth by companies’ CEOs, top managers and shareholders. It calls for more international business research investigating this link.