ArticleFrontiers in public health2023
Impact of COVID-19 on mental health in China: analysis based on sentiment knowledge enhanced pre-training and XGBoost algorithm.
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Mental health during COVID-19 through Online Photovoice (OPV) in China.Frontiers in public health · 2026Article
- Health perception and restorative experience in the therapeutic landscape of urban wetland parks during the COVID-19 pandemic.Frontiers in public health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) is causing a serious impact on the people living in countries across the entire world. The spread of this pandemic globally has led people worry every day about losing their jobs or even being threatened by the virus. This pandemic caused people to experience more serious psychological problems than we realized. However, there has been little research on how COVID-19 affects the mental health of the people. In this article, we attempted to use the social text data about COVID-19 on Sina Weibo (the largest "tweet" platform in China, and we will also call Weibo as tweet in the following content), to explore the impact of COVID-19 on the mental health of Chinese people. First, we fifilter the tweet data by selecting examples that contain COVID-19 and COVID-19 correlated keywords. However, we segment the filtered tweets, extract meaningful words, and construct a word vector sparse matrix as the measurement of every tweet. Then, for the model's labels, we use sentiment knowledge enhanced pre-training model (SKEP), a deep learning framework published by Baidu that measures the user's mental state. Through SKEP, we can obtain the probabilities of the user's positive and negative mental states. Finally, we use the XGBoost algorithm to study the relationship between the word vector sparse matrix and the mental health state of users. Our research shows that social text data can, indeed, reflect the mental health state of users to a large extent, and social data can be used to explore the impact of COVID-19 on mental health, which can help frame the public health policy.
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.