ArticleStudies in health technology and informatics2019
Analyzing Social Media Data to Understand Consumer Information Needs on Dietary Supplements.
Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study.Npj mental health research · 2024Article
- Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis.Journal of medical Internet research · 2023Article
- What are the ways in which social media is used in the context of complementary and alternative medicine in the health and medical scholarly literature? a scoping review.BMC complementary medicine and therapies · 2023Article
- Changes in the Phytochemical Profile and Antioxidant Properties ofInternational journal of molecular sciences · 2022Article
- Garcinia cambogia, Either Alone or in Combination With Green Tea, Causes Moderate to Severe Liver Injury.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2022Article
- A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts.Frontiers in sociology · 2022Article
- When text simplification is not enough: could a graph-based visualization facilitate consumers' comprehension of dietary supplement information?JAMIA open · 2021Article
- Analysis of Twitter to Identify Topics Related to Eating Disorder Symptoms.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
- Understanding information needs during COVID-19: A comparison study between an online health community and a Q&A platform.Health informatics journalArticle
- Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts.JMIR infodemiologyArticle
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.
Funding
Abstract
Despite the high consumption of dietary supplements (DS), few reliable, relevant, and comprehensive online resources could satisfy information seekers. This research study aims to understand consumer information needs on DS using topic modeling, and to evaluate accuracy in correctly identifying topics from social media. We retrieved 16,095 unique questions posted on Yahoo! Answers relating to 438 unique DS ingredients mentioned in sub-section, "Alternative medicine" under the section, "Health" . We implemented an unsupervised topic modeling method, Correlation Explanation (CorEx) to unveil the various topics in which consumers are most interested. We manually reviewed the keywords of all the 200 topics generated by CorEx and assigned them to 38 health-related categories, corresponding to 12 higher-level groups. We found high accuracy (90-100%) in identifying questions that correctly align with the selected topics. The results could guide us to generate a more comprehensive and structured DS resource based on consumers' information needs.
Indexed as
Identifiers
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.