ArticleScientific reports2023
Public concerns and attitudes towards autism on Chinese social media based on K-means algorithm.
Article in Scientific reports, 2023. 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.
- Large Language Model-Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities: Implications for Parent Education and Digital Support.Journal of medical Internet research · 2026Article
- Sentiment and Topic Analysis of Autism Spectrum Disorder Discussions on Chinese Social Media: Evidence From Bilibili and Rednote.Journal of autism and developmental disorders · 2026Article
- Computational text analysis of emotional expressions related to non-suicidal self-injury in Chinese social media.Scientific reports · 2026Article
- Topic Modeling of Social Media Discourse of Autism Support Groups.Behavioral sciences (Basel, Switzerland) · 2026Article
- Comparative sentiment and thematic analysis of depression-related discourse between healthcare professionals and non-healthcare individuals on Chinese social media: a computational text analysis study.Frontiers in psychology · 2026Article
- Analyzing the capability description of testing institution in Chinese phrase using a joint approach of semi-supervised K-Means clustering and BERT.Scientific reports · 2025Article
- Healthcare professionals and the public sentiment analysis of ChatGPT in clinical practice.Scientific reports · 2025Article
- Sentiment analysis of cancer screening in Chinese social media: Qualitative studies based on machine learning.PloS one · 2025Article
- Application of unsupervised clustering model based on graph embedding in water environment.Scientific reports · 2023Article
- Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To investigate the hot topics and attitudes of autism in the larger community. In this study, we analyzed and summarized experimental texts from the social media platform Zhihu using the TF-IDF algorithm and K-means clustering approach. Based on the analysis of the 1,740,826-word experimental text, we found that the popularity of autism has steadily risen over recent years. Sufferers and their parents primarily discuss autism. The K-means clustering algorithm revealed that the most popular topics are divided into four categories: self-experience of individuals with autism, external views of individuals with autism, caring and stressful behaviors of caregivers, and information about autism. This study concluded that people with autism face more incredible negative emotions, external cognitive evaluations of the autistic group reflect stereotypes, the caregiver's family suffers high financial and psychological stress, and disorders caused by disease in autistic individuals.
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.