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Abstract: We explore what associations Norwegian citizens have with the Sustainable Development Goals (SDGs) through an analysis of their knowledge of and attitudes towards these goals. To achieve this, we combine three methodological approaches: (1) structural topic modelling (STM) providing topic prevalence, (2) manual content analysis including exploration of attitudes, and (3) analysis of how individuals' background characteristics relate to expressions of negative or positive sentiments towards SDGs. The data consist of 4046 answers to an open-ended question, formulated as follows: What comes to mind when you read or hear the expression 'UN Sustainability Development Goals?', fielded during the autumn of 2020, through a survey at the Norwegian Citizen Panel/DIGSSCORE. Major findings: The most prevalent topics associated with the SDGs are poverty, climate/environment, resources, future generations and consumption. The analysis indicates that the Norwegian awarenessraising campaigns have been relatively successful. However, the manual analysis shows that the SDG is an unknown concept for 12% of the respondents, and that 10% hold a negative view. Nine percent of the respondents hold a positive view of the goals. In addition, their attitudes differ clearly according to various background variables (gender, age, political preference). The findings are important for further efforts to spread knowledge of, and raise interest in, the SDGs, at different levels (government,regional and local contexts). Keywords: sustainability; open-ended question; Structural Topic Modelling; awareness; attitudes. ; publishedVersion

We explore what associations Norwegian citizens have with the Sustainable Development Goals (SDGs) through an analysis of their knowledge of and attitudes towards these goals. To achieve this, we combine three methodological approaches: (1) structural topic modelling (STM) providing topic prevalence, (2) manual content analysis including exploration of attitudes, and (3) analysis of how individuals' background characteristics relate to expressions of negative or positive sentiments towards SDGs. The data consist of 4046 answers to an open-ended question, formulated as follows: What comes to mind when you read or hear the expression 'UN Sustainability Development Goals?', fielded during the autumn of 2020, through a survey at the Norwegian Citizen Panel/DIGSSCORE. Major findings: The most prevalent topics associated with the SDGs are poverty, climate/environment, resources, future generations and consumption. The analysis indicates that the Norwegian awareness-raising campaigns have been relatively successful. However, the manual analysis shows that the SDG is an unknown concept for 12% of the respondents, and that 10% hold a negative view. Nine percent of the respondents hold a positive view of the goals. In addition, their attitudes differ clearly according to various background variables (gender, age, political preference). The findings are important for further efforts to spread knowledge of, and raise interest in, the SDGs, at different levels (government, regional and local contexts).

The recent years have witnessed major releases of frameworks and tools to democratize deep learning to the masses. One of those is PyTorch, a Python-based framework that also provides an extensive specialised Question-and-Answer (Q\&A) platform for developers. Q\&A platforms have become essential resources to modern software development practices, yet most research focuses on general-interest Q\&As, such as Stack Overflow. This poses a gap in the research, as previous investigations do not study library-specific issues nor explore discussions for different versions of a framework such as PyTorch. To breach this gap, we analyze PyTorch Discussion Forums to uncover developers' main issues and discussion trends across different PyTorch versions. To do this, we leveraged Adaptive Online Biterm Topic Modeling (AOBTM), identified emerging topics, and confirmed results through a manual study. We were able to successfully predict which topics would arise on the next version of PyTorch, verifying this with `future' slices, even determining differences in percentage of emergence across versions. This result can forecast the community's trends regarding future topics, and also provide a variety emerging topics that users discuss in the `uncategorized' group of posts on PyTorch Discussion Forums; which can benefit the PyTorch framework developers and the users' community.

The recent years have witnessed major releases of frameworks and tools to democratize deep learning to the masses. One of those is PyTorch, a Python-based framework that also provides an extensive specialised Question-and-Answer (Q\&A) platform for developers. Q\&A platforms have become essential resources to modern software development practices, yet most research focuses on general-interest Q\&As, such as Stack Overflow. This poses a gap in the research, as previous investigations do not study library-specific issues nor explore discussions for different versions of a framework such as PyTorch. To breach this gap, we analyze PyTorch Discussion Forums to uncover developers' main issues and discussion trends across different PyTorch versions. To do this, we leveraged Adaptive Online Biterm Topic Modeling (AOBTM), identified emerging topics, and confirmed results through a manual study. We were able to successfully predict which topics would arise on the next version of PyTorch, verifying this with `future' slices, even determining differences in percentage of emergence across versions. This result can forecast the community's trends regarding future topics, and also provide a variety emerging topics that users discuss in the `uncategorized' group of posts on PyTorch Discussion Forums; which can benefit the PyTorch framework developers and the users' community.

"Structural Equation Modelling (SEM) is a relatively recently developed statistical technique based upon factor analysis and multiple regression. This review will cover four of the most widely used (in psychology departments) packages, looking in particular at their suitability for use in a teaching environment, rather than an in depth look at their technical capabilities: LISREL 8.20, EQS 5.6, AMOS 3.6, SEPath. The four programs reviewed were all tested running Windows 95, on a 166 MHz Pentium, with 32 MB RAM." (author's abstract)

AbstractWith an increased focus on social well‐being in response to a burgeoning global economy exposing the weaknesses of social welfare policies, research output in the field has grown exponentially. Keeping track of the evolving research themes proves difficult due to the steady rise in the number of studies published in the interdisciplinary field of social welfare. Therefore, researchers need a comprehensive overview to confirm the current shape of the field based on the published research. Using a latent Dirichlet allocation algorithm as a topic modelling technique, this study identified 12 prominent themes from more than 10,000 research outputs on social welfare published from 2000 to 2020 in Scopus‐indexed journals. Such an exploratory text‐mining approach to literature review provides broad insights into the diversity of research and may serve as a foundation for further in‐depth studies. Identifying these 12 thematic areas and their sub‐themes allows us to articulate the complexity and diversity of social welfare issues, which go far beyond the field of well‐established welfare economics or social work. The study shows that the topic of 'social welfare' has not only evolved over time but has significantly broadened its meaning. It can no longer be solely synonymous with institutional social security. We contend that research in this area needs to take into account a broader and more systematic range of determinants constituting the dynamic character of social welfare.

While scholars have already identified and discussed some of the most urgent problems in content moderation in the Global North, fewer scholars have paid attention to content regulation in the Global South, and notably Africa. In the absence of content moderation by Western tech giants themselves, African countries appear to have shifted their focus towards state-centric approaches to regulating content. We argue that those approaches are largely informed by a regime's motivation to repress media freedom as well as institutional constraints on the executive. We use structural topic modelling on a corpus of news articles worldwide (N = 7′787) mentioning hate speech and fake news in 47 African countries to estimate the salience of discussions of legal and technological approaches to content regulation. We find that, in particular, discussions of technological strategies are more salient in regimes with little respect for media freedom and fewer legislative constraints. Overall, our findings suggest that the state is the dominant actor in shaping content regulation across African countries and point to the need for a better understanding of how regime-specific characteristics shape regulatory decisions.

Unidad de excelencia María de Maeztu CEX2019-000940-M ; Carbon taxes evoke a variety of public responses, often with negative implications for policy support, implementation and stringency. Here we use topic modelling to analyze associations of Spanish citizens with a policy proposal to introduce a carbon tax. This involves asking two key questions, to elicit (1) citizens' associations with a carbon tax and (2) their judgment of the fairness of such a policy for distinct uses of tax revenues. We identify 11 topics for the first question and 18 topics for the second. We perform regression analysis to assess how respondents' associations relate to their carbon-tax acceptability, knowledge and socio-demographic characteristics. The results show that, compared to people accepting the carbon tax, those rejecting it show less trust in politicians, think that the rich should pay more than the poor, consider the tax to be less fair, and stress more a lack of renewable energy or low-carbon transport. Respondents accepting a carbon tax emphasize more the need to solve environmental problems and care about a just society. These insights can help policy makers to improve the design and communication of climate policy with the aim to increase its public acceptability.