ArticleFrontiers in research metrics and analytics2023
A comparison of three methods to determine the subject matter in textual data.
Article in Frontiers in research metrics and analytics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Diagnostic Reasoning in General Internal Medicine Consultations: A Text Mining Analysis of Consultation Requests.Journal of general and family medicine · 2026Article
- Analytic Methods to Investigate HIV-Related Digital Communication: A Scoping Review.Current HIV/AIDS reports · 2025Article
- Dissonance between posts of health agencies and public comments regarding COVID-19 and vaccination on Facebook in Northern California.BMC public health · 2024Article
- Cascaded Semantic Fractionation for identifying a domain in social media.Frontiers in research metrics and analytics · 2024Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This study compares three different methods commonly employed for the determination and interpretation of the subject matter of large corpuses of textual data. The methods reviewed are: (1) topic modeling, (2) community or group detection, and (3) cluster analysis of semantic networks. Two different datasets related to health topics were gathered from Twitter posts to compare the methods. The first dataset includes 16,138 original tweets concerning HIV pre-exposure prophylaxis (PrEP) from April 3, 2019 to April 3, 2020. The second dataset is comprised of 12,613 tweets about childhood vaccination from July 1, 2018 to October 15, 2018. Our findings suggest that the separate "topics" suggested by semantic networks (community detection) and/or cluster analysis (Ward's method) are more clearly identified than the topic modeling results. Topic modeling produced more subjects, but these tended to overlap. This study offers a better understanding of how results may vary based on method to determine subject matter chosen.
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