Environmental Sociology Key Topics in environmental sociology, 1990–2014: results from a computational text analysis (original) (raw)
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Key Topics in environmental sociology, 1990–2014: results from a computational text analysis
Environmental sociology is a growing field producing a diverse body of literature while also moving into the mainstream of the larger discipline. The twin goals of this paper are to introduce environmental sociologists to innovations in content analysis, specifically a form of text-mining known as topic modeling, and then employing it to identify key themes and trends within our diverse field. We apply the topic modeling approach to a corpus of research articles within environmental sociology, identifying 25 central topics within the field and examining their prevalence over time, co-occurrence, impact (judged by citations), and prestige (judged by journal rankings). Our results indicate which topics are most prevalent, tend to occur together, and how both vary over time. They also indicate that the highest impact topics are not the most prevalent, the most prestigious topics are not the most prevalent, and topics can be prestigious without exerting much impact. We conclude with a discussion of the capabilities computational text analysis methods offer environmental sociologists.
Humanities and Social Sciences Communications
Human mobility is increasingly associated with environmental and climatic factors. One way to explore how mobility and the environment are linked is to review the research on different aspects of the topic. However, so many relevant articles are published that analysis of the literature using conventional techniques is becoming prohibitively arduous. To overcome this constraint, we have applied automated textual analysis. Using unsupervised topic modelling on 3197 peer-reviewed articles on the nexus between mobility and the environment published over the last 30 years, we identify 37 major topics. Based on their language use, the topics were deeply branched into two categories of focus: Impact and Adaptation. The Impact theme is further clustered into sub-themes on vulnerability and residential mobility, while articles within the Adaptation theme are clustered into governance, disaster management and farming. The analysis revealed opportunities for greater collaboration within envir...
Sustainability, 2021
Facing the big data wave, this study applied artificial intelligence to cite knowledge and find a feasible process to play a crucial role in supplying innovative value in environmental education. Intelligence agents of artificial intelligence and natural language processing (NLP) are two key areas leading the trend in artificial intelligence; this research adopted NLP to analyze the research topics of environmental education research journals in the Web of Science (WoS) database during 2011–2020 and interpret the categories and characteristics of abstracts for environmental education papers. The corpus data were selected from abstracts and keywords of research journal papers, which were analyzed with text mining, cluster analysis, latent Dirichlet allocation (LDA), and co-word analysis methods. The decisions regarding the classification of feature words were determined and reviewed by domain experts, and the associated TF-IDF weights were calculated for the following cluster analysi...
Ecological Informatics, 2019
Ecological disturbances influence how ecosystems function and evolve over time by creating changes in local conditions that potentially lead to larger-scale impacts. Disturbances such as wildfires, flooding, and windstorms can occur quickly, causing abrupt shifts in ecosystem processes that endure for several years (Thom et al., 2013). Slower-moving disturbances, such as outbreaks of insects that kill or weaken host organisms, can occur over longer periods and exert more gradual ecosystem impacts that influence ecological processes for decades (Raffa et al., 2008). Regardless of how they operate, the frequency of natural disturbance events has been increasing around the world in recent decades as a consequence of climate change and anthropogenic alterations to both terrestrial and aquatic environments (Flannigan et al., 2000, Johnstone et al., 2016, Seidl et al., 2017). The rise in global temperatures, coupled with loss of biodiversity due to natural resource extraction and land use change, have heightened the impacts that disturbances exert on a number of ecological processes, sometimes causing ecosystems to transition into novel and often undesirable states (Parks et al., 2016). The continued sprawl of urban areas into natural environments has also amplified the number of people at risk to natural disturbances (Liu et al., 2015). As a result, environmental policies, particularly those aimed at minimizing disturbance impacts through mitigation or adaptation strategies, are being critiqued for their inability to ensure both long-term sustainability of natural resource use as well as address the risk of disturbances to human populations (Keskitalo et al., 2016, Six et al., 2014). Researchers conducting environmental policy analysis can critically examine both discursive and substantive elements of disturbance-related policies as they seek to understand the degree to which these are informed by various scientific and political perspectives. Recent scholarship in environmental policy analysis suggests that ecological disturbances may be particularly fertile ground for opposition over what is termed "problem definition" (Fifer and Orr, 2013)-including the scope and urgency of the issue, causal factors, and culpability (including human actors, policies, or practices)-and the policy responses that logically flow from particular problem definitions (
Reflecting trends in the academic landscape of sustainable energy using probabilistic topic modeling
Energy, Sustainability and Society, 2019
Background: Facing planetary boundaries, we need a sustainable energy system providing its life support function for society in the long-term within environmental limits. Since science plays an important role in decision-making, this study examines the thematic landscape of research on sustainable energy, which may contribute to a sustainability transformation. Understanding the structure of the research field allows for critical reflections and the identification of blind spots for advancing this field. Methods: The study applies a text mining approach on 26533 Scopus-indexed abstracts published from 1990 to 2016 based on a latent Dirichlet allocation topic model. Models with up 1100 topics were created. Based on coherence scores and manual inspection, the model with 300 topics was selected. These statistical methods served for highlighting timely topic trends, differing thematic fields, and emerging communities in the topic network. The study critically reflects the quantitative results from a sustainability perspective.
Probabilistic topic modelling is a machine learning technique that has recently begun to find application in the social sciences. With almost no human supervision, probabilistic topic models can infer the thematic structure of large textual datasets, making them an appealing tool for scholars in fields such as communication studies, where such datasets are increasingly common. However, topic models also present social scientists with a range of conceptual and practical challenges, many of which are yet to be satisfactorily resolved. Far from making life simpler for social scientists, the outputs of topic models can be bewildering, not only because of their complexity-a model may include dozens of topics, each of which is defined by dozens of terms-but also because of their multiplicity, since a topic model can produce not one, but infinitely many, subtly different sets of topics to describe a given dataset. Further difficulties arise from the complexity of the data itself: in social science, textual datasets often represent diverse assemblages of actors, and the meaning of the texts may depend on the circumstances of their production as much as on their textual content.
Book title: Data Mining Applications with R Editors: Yanchang Zhao, Yonghua Cen Publisher: Elsevier Publish date: December 2013 ISBN: 978-0-12-411511-8 , 2013
The aim of this chapter is to show some basic methods using R to analyze text content to discover emergent issues and controversies in diverse corpora. As a specific case study, I investigate the culture of microblogging academics within the dynamics of a professional conference to gain insights into the key issues and debates emergent in this community and the transformative effects of using Twitter in academic contexts. Microblogging academics can be considered a type of online community which has its own norms, rules, and communicative behaviors (Gruzd et al., 2011) that can be analyzed with anthropological methods (cf. Boellstorff, 2011; Wilson and Peterson, 2002). My hypothesis is that data mining the publically available microblog text content generated in relation to the 109th Annual Meeting of the American Anthropological Association (AAA) in November 2011 can reveal the main issues and controversies that characterized the event as well as the community structure of the people generating the corpus. Although the duration of the meeting represents a narrow slice of Twitter content, it is ideal for looking at which academics are tweeting and why they tweet because academic meetings are a period of highly concentrated intellectual and social activity within the academic community. It is during these times that the distinctive patterns of shared learned knowledge, behaviors, and beliefs that characterize communities are most apparent (Egri, 1992). It is hoped that the methods presented will be suitable for the analysis of a wide variety of communities that generate large amounts of text content.
Identifying dominant topics appearing in the Journal of Cleaner Production
Journal of Cleaner Production, 2018
The number of publications in the field of sustainability research has increased rapidly in recent decades and the research topics have multiplied dramatically. It has become difficult to keep track of this highly dynamic field of research. In order to explore the possibilities of computer-aided automated text and meaning capture for the field of sustainability research, we are testing in this paper the method of Latent Semantic Analysis (LSA) with regard to the text corpus published by the Journal of Cleaner Production since 1995. We present the discernible topics identified by this method both in their statistical concept composition and in their temporal evolution and analyze individual, randomly selected contributions in relation to their thematic position in the overall corpus. In particular, the latter gives hope that text mining methods like the here applied LSA could help human readers in the future to maintain an overview in large text corpora and to categorize individual contributions thematically. In this study, as regards content, we identified sustainability education as crucial topic for sustainable development and, additionally, that life-cycle analyses are significantly gaining importance in recent years.
Mapping Climate Themes From 2008-2021—An Analysis of Business News Using Topic Models
IEEE Access
India and other developing economies are receiving more attention in the context of climate change due to their rapid rates of economic expansion and large populations. In terms of absolute emissions, India surpassed China and the U.S. in 2018 to become the third-largest emitter. Solving this wicked problem calls for climate action across the stakeholder spectrum involving governments, business communities, and citizens. While extant literature has focused significantly on the role of governments and individual perceptions, the business sector needs to be more represented. In this study, we consider business news media as a platform that reflects the industry engagement in climate change and as a source of information on climate change for business decision-makers. Hence, understanding the topic and themes in the nexus of climate and business is important to evaluate the business sector's stance towards climate change and how it has evolved. This work explores business news related to climate change using natural language techniques. We first experiment with three topic-modeling techniques, such as LDA, NMF, and BERTopic, on the business news and two more benchmark news datasets. Our test data is derived from digital news archives of 'The Economic Times-India's leading business news daily. We evaluate the performance based on quantitative metrics commonly used for topic models. We choose the algorithm that provides the highest precision for climate-specific information represented by the test dataset. We then apply the algorithm with the best performance, as evaluated by the experiment, to a large corpus of Indian climate news from E.T. spanning from 2008-2021. We present how different themes, including industry engagement, evolved over the last two decades. The results suggest that climate cooperation has the highest contribution in the corpus, with other themes on resource management, energy and business gaining traction in recent years. INDEX TERMS Climate change, media, topic models, NLP, computational social sciences, experiment.
Trends and gaps in biodiversity and ecosystem services research: A text mining approach
Ambio
Understanding the relationship between biodiversity conservation and ecosystem services concepts is essential for evidence-based policy development. We used text mining augmented by topic modelling to analyse abstracts of 15 310 peer-reviewed papers (from 2000 to 2020). We identified nine major topics; “Research & Policy”, “Urban and Spatial Planning”, “Economics & Conservation”, “Diversity & Plants”, “Species & Climate change”, “Agriculture”, “Conservation and Distribution”, “Carbon & Soil & Forestry”, “Hydro-& Microbiology”. The topic “Research & Policy” performed highly, considering number of publications and citation rate, while in the case of other topics, the “best” performances varied, depending on the indicator applied. Topics with human, policy or economic dimensions had higher performances than the ones with ‘pure’ biodiversity and science. Agriculture dominated over forestry and fishery sectors, while some elements of biodiversity and ecosystem services were under-represe...