ArticleBMC public health2016
Text mining for identifying topics in the literatures about adolescent substance use and depression.
Article in BMC public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 101 citations in OpenAlex.
- Use of autobiographical stimuli as a mood manipulation procedure: Systematic mapping review.PloS one · 2022Pooled it
- Challenging suicide, burnout, and depression among veterinary practitioners and students: text mining and topics modelling analysis of the scientific literature.BMC veterinary research · 2021Pooled it
- Current trends in cancer immunotherapy: a literature-mining analysis.Cancer immunology, immunotherapy : CII · 2020Pooled it
- Analysis of the causes of improper medical decision-making in medical damage liability disputes in China: a text mining approach.BMC health services research · 2025Article
- Ecosystem Services Linked to Extensive Sheep and Goat Farming in Mountain Areas: A Global Literature Analysis Using Text Mining and Topic Analysis.Animals : an open access journal from MDPI · 2025Article
- AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Text mining of hypertension researches in the west Asia region: a 12-year trend analysis.Renal failure · 2024Article
- Enteric Methane Emission in Livestock Sector: Bibliometric Research from 1986 to 2024 with Text Mining and Topic Analysis Approach by Machine Learning Algorithms.Animals : an open access journal from MDPI · 2024Review
- Canine Bacterial Endocarditis: A Text Mining and Topics Modeling Analysis as an Approach for a Systematic Review.Microorganisms · 2024Review
- Exploratory bibliometric analysis and text mining to reveal research trends in cardiac aging.Aging medicine (Milton (N.S.W)) · 2024Article
- Unlocking insights: text mining analysis on the health, welfare, and behavior of cows in automated milking systems.Journal of animal science · 2024Article
- A Five-Decade Text Mining Analysis of Cochlear Implant Research: Where We Started and Where We Are Heading.Medicina (Kaunas, Lithuania) · 2023Article
- Development of a prediction model for the depression level of the elderly in low-income households: using decision trees, logistic regression, neural networks, and random forest.Scientific reports · 2023Article
- Dromedary camel's welfare: literature from 1980 to 2023 with a text mining and topic analysis approach.Frontiers in veterinary science · 2023Review
- Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review.Journal of medical Internet research · 2022Article
- Health Communication through Chinese Media on E-Cigarette: A Topic Modeling Approach.International journal of environmental research and public health · 2022Article
- Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set.JMIR medical informatics · 2022Article
- Perceived barriers to psychiatric help-seeking in South Korea by age groups: text mining analyses of social media big data.BMC psychiatry · 2022Article
- Changes inClinical and experimental gastroenterology · 2022Article
- Text mining of Reddit posts: Using latent Dirichlet allocation to identify common parenting issues.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBoth adolescent substance use and adolescent depression are major public health problems, and have the tendency to co-occur. Thousands of articles on adolescent substance use or depression have been published. It is labor intensive and time consuming to extract huge amounts of information from the cumulated collections. Topic modeling offers a computational tool to find relevant topics by capturing meaningful structure among collections of documents.
methodsIn this study, a total of 17,723 abstracts from PubMed published from 2000 to 2014 on adolescent substance use and depression were downloaded as objects, and Latent Dirichlet allocation (LDA) was applied to perform text mining on the dataset. Word clouds were used to visually display the content of topics and demonstrate the distribution of vocabularies over each topic.
resultsThe LDA topics recaptured the search keywords in PubMed, and further discovered relevant issues, such as intervention program, association links between adolescent substance use and adolescent depression, such as sexual experience and violence, and risk factors of adolescent substance use, such as family factors and peer networks. Using trend analysis to explore the dynamics of proportion of topics, we found that brain research was assessed as a hot issue by the coefficient of the trend test.
conclusionsTopic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.