ArticleThe annals of applied statistics2024
A QUANTITATIVE LINGUISTIC ANALYSIS OF A CANCER ONLINE HEALTH COMMUNITY WITH A SMOOTH LATENT SPACE MODEL.
Article in The annals of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A QUANTITATIVE LINGUISTIC ANALYSIS OF A CANCER ONLINE HEALTH COMMUNITY WITH A SMOOTH LATENT SPACE MODEL.The annals of applied statistics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Online health communities (OHCs) provide free, open, and well-resourced platforms for patients, family members, and others to discuss illnesses, express feelings, and connect with others. Linguistic analysis of OHC posts can assist in better understanding disease conditions as well as monitoring the emotional and mental status of patients and those who are closely related. Many existing OHC linguistic analyses are limited by focusing on individual words. There are a handful of cooccurrence network analyses, which have multiple methodological limitations. In this article we analyze posts that are publicly available at the LUNGevity Foundation's Lung Cancer Support Community (LCSC). The analyzed data contains 21,028 posts published between April 2018 and February 2022. For word cooccurrence network analysis, we develop a two-part latent space model, which advances from the existing ones by accommodating network weights. Further, we consider the scenario where there are change points in time, networks remain the same between two change points but differ on the two sides of a change point, and the number and locations of change points are unknown. A penalized fusion approach is developed to data-dependently determine change points and estimate networks. In data analysis multiple change points are identified, which reflect significant changes in lung cancer patients' and their close affiliates' emotional/mental status and mostly align with the changes in COVID-19. The obtained network structures and other findings are also sensible.
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.