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Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals

ABSTRACT: The United Nations’ (UN) Sustainable Development Goals (SDGs) are heterogeneous and interdependent, comprising 169 targets and 231 indicators of sustainable development in such diverse areas as health, the environment, and human rights. Existing efforts to map relationships among SDGs are either theoretical investigations of sustainability concepts, or empirical analyses of development indicators and policy simulations. We present an alternative approach, which describes and quantifies the complex network of SDG interdependencies by applying computational methods to policy and scientific documents. Methods of Natural Language Processing are used to measure overlaps in international policy discourse around SDGs, as represented by the corpus of all existing UN progress reports about each goal (N = 85 reports). We then examine if SDG interdependencies emerging from UN discourse are reflected in patterns of integration and collaboration in SDG-related science, by analyzing data on all scientific articles addressing relevant SDGs in the past two decades (N = 779,901 articles). Results identify a strong discursive divide between environmental goals and all other SDGs, and unexpected interdependencies between SDGs in different areas. While UN discourse partially aligns with integration patterns in SDG-related science, important differences are also observed between priorities emerging in UN and global scientific discourse. We discuss implications and insights for scientific research and policy on sustainable development after COVID-19.

SUBMITTER: Smith T

PROVIDER: S-EPMC8599416 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Perceptions of tobacco, cannabis, and electronic nicotine delivery systems (ENDS) are continually evolving in the United States. Exploring these characteristics through user generated text sources may provide novel insights into product use behavior that are challenging to identify using survey-based methods. The objective of this study was to compare the topics frequently discussed among Reddit members in cannabis, tobacco, and ENDS-specific subreddits. Methods: We collected 643,070 posts on the social media site Reddit between January 2013 and December 2018. We developed and validated an annotation scheme, achieving a high level of agreement among annotators. We then manually coded a subset of 2,630 posts for their content with relation to experiences and use of the three products of interest, and further developed word cloud representations of the words contained in these posts. Finally, we applied Latent Dirichlet Allocation (LDA) topic modeling to the 643,070 posts to identify emerging themes related to cannabis, tobacco, and ENDS products being discussed on Reddit. Results: Our manual annotation process yielded 2,148 (81.6%) posts that contained a mention(s) of either cannabis, tobacco, or ENDS with 1,537 (71.5%) of these posts mentioning cannabis, 421 (19.5%) mentioning ENDS, and 264 (12.2%) mentioning tobacco. In cannabis-specific subreddits, personal experiences with cannabis, cannabis legislation, health effects of cannabis use, methods and forms of cannabis, and the cultivation of cannabis were commonly discussed topics. The discussion in tobacco-specific subreddits often focused on the discussion of brands and types of combustible tobacco, as well as smoking cessation experiences and advice. In ENDS-specific subreddits, topics often included ENDS accessories and parts, flavors and nicotine solutions, procurement of ENDS, and the use of ENDS for smoking cessation. Conclusion: Our findings highlight the posting and participation patterns of Reddit members in cannabis, tobacco, and ENDS-specific subreddits and provide novel insights into aspects of personal use regarding these products. These findings complement epidemiologic study designs and highlight the potential of using specific subreddits to explore personal experiences with cannabis, ENDS, and tobacco products.

Project description:BackgroundSocial media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities.ObjectiveThis study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed.MethodsFirst, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term #autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term "autism" in PubMed without time restriction. Lastly, we compared the results of the 2 databases.ResultsWe retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior.ConclusionsThis study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries.

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Natural language processing and network analysis provide novel insights on policy and scientific discourse around Sustainable Development Goals

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