ArticleFrontiers in public health2019
Assessing Canadians Health Activity and Nutritional Habits Through Social Media.
Article in Frontiers in public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 36 citations in OpenAlex.
- Parsing Instagram Posts for Smoking Imagery Amongst Users: A Content Analysis.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine · 2026Article
- Real-time prediction of HFNC treatment failure in acute hypoxemic respiratory failure using machine learning.Scientific reports · 2025Article
- Ensemble Model for Diagnostic Classification of Alzheimer's Disease Based on Brain Anatomical Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- Review
- Resilience of political leaders and healthcare organizations during COVID-19.PeerJ. Computer science · 2022Article
- Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology.Advances in nutrition (Bethesda, Md.) · 2021Article
- Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours.Public health nutrition · 2021Review
- Implications of Twitter in Health-Related Research: A Landscape Analysis of the Scientific Literature.Frontiers in public health · 2021Article
- Utilizing deep learning and graph mining to identify drug use on Twitter data.BMC medical informatics and decision making · 2020Article
- Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.JMIR public health and surveillance · 2020Review
- Ifood: Development and usability study of a social media-based applet for dietary monitoring.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When conducting data analysis in the twenty-first century, social media is crucial to the analysis due to the ability to provide information on a variety of topics such as health, food, feedback on products, and many others. Presently, users utilize social media to share their daily lifestyles. For example, travel locations, exercises, and food are common subjects of social media posts. By analyzing such information collected from users, health of the general population can be gauged. This analysis can become an integral part of federal efforts to study the health of a nation's people on a large scale. In this paper, we focus on such efforts from a Canadian lens. Public health is becoming a primary concern for many governments around the world. It is believed that it is necessary to analyze the current scenario within a given population before creating any new policies. Traditionally, governments use a variety of ways to gauge the flavor for any new policy including door to door surveys, a national level census, or hospital information to decide health policies. This information is limited and sometimes takes a long time to collect and analyze sufficiently enough to aid in decision making. In this paper, our approach is to solve such problems through the advancement of natural language processing algorithms and large scale data analysis. Our in-depth results show that the proposed method provides a viable solution in less time with the same accuracy when compared to traditional methods.
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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.