What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016

There is surprisingly little known about agenda setting for international development in the United Nations (UN) despite it having a significant influence on the process and outcomes of development efforts. This paper addresses this shortcoming using a novel approach that applies natural language processing techniques to countries' annual statements in the UN General Debate. Every year UN member states deliver statements during the General Debate on their governments' perspective on major issues in world politics. These speeches provide invaluable information on state preferences on a wide range of issues, including international development, but have largely been overlooked in the study of global politics. This paper identifies the main international development topics that states raise in these speeches between 1970 and 2016, and examine the country-specific drivers of international development rhetoric

Explaining topic prevalence in answers to open-ended survey questions about climate change

Citizens’ opinions are crucial for action on climate change, but are, owing to the complexity of the issue, diverse and potentially unformed1. We contribute to the understanding of public views on climate change and to knowledge needed by decision-makers by using a new approach to analyse answers to the open survey question ‘what comes to mind when you hear the words ‘climate change’?’. We apply automated text analysis, specifically structural topic modelling2, which induces distinct topics based on the relative frequencies of the words used in 2,115 responses. From these data, originating from the new, nationally representative Norwegian Citizen Panel, four distinct topics emerge: Weather/Ice, Future/Impact, Money/Consumption and Attribution. We find that Norwegians emphasize societal aspects of climate change more than do respondents in previous US and UK studies3, 4, 5, 6. Furthermore, variables that explain variation in closed questions, such as gender and education, yield different and surprising results when employed to explain variation in what respondents emphasize. Finally, the sharp distinction between scepticism and acceptance of conventional climate science, often seen in previous studies, blurs in many textual responses as scepticism frequently turns into ambivalence.acceptedVersio

Machine learning and the identification of Smart Specialisation thematic networks in Arctic Scandinavia

The European Union (EU) has recognized that universities and research institutes play a critical role in regional Smart Specialisation processes. Our research aims to identify thematic cross-border research domains across space and disciplines in Arctic Scandinavia. We identify potential domains using an unsupervised machine-learning technique (topic modelling). We uncover latent topics based on similarities in the vocabulary of research papers. The proposed methodology can be utilized to identify common research domains across regions and disciplines in almost real time, thereby acting as a decision support system to facilitate cooperation among knowledge producers

Psycholinguistic changes in the communication of adolescent users in a suicidal ideation online community during the COVID-19 pandemic

Since the outbreak of the COVID-19 pandemic, increases in suicidal ideation and suicide attempts in adolescents have been registered. Many adolescents experiencing suicidal ideation turn to online communities for social support. In this retrospective observational study, we investigated the communication—language style, contents and user activity—in 7975 unique posts and 51,119 comments by N = 2862 active adolescent users in a large suicidal ideation support community (SISC) on the social media website reddit.com in the onset period of the COVID-19 pandemic. We found significant relative changes in language style markers for hopelessness such as negative emotion words (+ 10.00%) and positive emotion words (− 3.45%) as well as for social disengagement such as social references (− 8.63%) and 2nd person pronouns (− 33.97%) since the outbreak of the pandemic. Using topic modeling with Latent Dirichlet Allocation (LDA), we identified significant changes in content for the topics Hopelessness (+ 23.98%), Suicide Methods (+ 17.11%), Social Support (− 14.91%), and Reaching Out to users (− 28.97%). Changes in user activity point to an increased expression of mental health issues and decreased engagement with other users. The results indicate a potential shift in communication patterns with more adolescent users expressing their suicidal ideation rather than relating with or supporting other users during the COVID-19 pandemic

Technologie und Behinderung im Wandel: Themen und Entwicklungen

Die Technologie für Menschen mit Behinderungen wird kontinuierlich weiterentwickelt und es erscheinen immer mehr wissenschaftliche Veröffentlichungen zu diesem Themenkomplex. Den Überblick zu behalten und die neuesten Entwicklungen nachzuvollziehen, wird für Wissenschaftler*innen und Praktiker*innen zunehmend aufwändig. Bisherige Literaturübersichten analysieren oft nur Publikationen aus einem Jahr oder einer Zeitschrift, was einen umfassenden Überblick erschwert. In unserem Beitrag haben wir mit einem automatisierten Verfahren eine Literaturübersicht über einen Zeitraum von mehr als 20 Jahren erstellt. Mit Hilfe des Topic Modeling haben wir mehr als 10.000 Zeitschriftenartikel zum Themenkomplex Technologie und Behinderung analysiert und dabei verschiedene Themenschwerpunkte identifiziert. Es wird deutlich, dass sich der Themenkomplex in den Forschungsthemen und -trends seit dem Jahr 2000 in vielen Bereichen ausdifferenziert hat. Dieser Überblick hilft Forschenden, in der Praxis tätigen Personen und auch Studierenden, einen Überblick über das Fach und seine Entwicklungen zu gewinnen.The field of technology for individuals with disabilities is continuously advancing, and there is a growing body of scientific literature on this topic. However, it can be challenging for researchers and practitioners to stay up-to-date with the latest developments. Previous literature reviews have often focused on publications from a single year or journal, making it difficult to provide a comprehensive overview. This paper uses an automated process that allows for a more comprehensive analysis of the literature that spans more than two decades. Using topic modelling, we analysed over 10,000 journal articles on technology and disability and identified several key themes. It is evident that the research topics and trends within the field have become more diverse since 2000. This review aims to provide researchers, practitioners and students with a comprehensive understanding of the subject and how it's evolving

Media Attention to Environmental Issues and ESG Investing

We analyse how ESG scores affect future returns when environmental issues receive higher media coverage. Investors might take environmental aspects into account if they are confronted with the issue of global warming more frequently in the press. We assess the prevalence of environmental issues in the media with a machine learning-based Structural Topic Modelling (STM) methodology, using a news archive published in the USA. Running Fama-MacBeth regressions, we find that in periods when the media actively report on environmental issues, ESG scores have a significant negative impact on future returns, whereas, in months when fewer such articles are published, investors do not take sustainability measures into account, and ESG scores have no explanatory power

Preparedness for Data-Driven Business Model Innovation:A Knowledge Framework for Incumbent Manufacturers

This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.</p