ArticleProceedings. IEEE International Conference on Healthcare Informatics2019
Analysis of Twitter to Identify Topics Related to Eating Disorder Symptoms.
Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
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Who cites it
5 citing papers in PubMed.
- A Longitudinal Examination of the Mediating Role of Body Dissatisfaction in the Relationship Between Pornography Use Frequency and Eating Disturbances: A Cross-Lagged Mediation Model.Archives of sexual behavior · 2025Article
- Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis.JMIR infodemiology · 2023Article
- Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions.Journal of eating disorders · 2022Review
- Examining the Pro-Eating Disorders Community on Twitter Via the Hashtag #proana: Statistical Modeling Approach.JMIR mental health · 2021Article
- Exploring Eating Disorder Topics on Twitter: Machine Learning Approach.JMIR medical informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Eating disorders (EDs) are serious mental illnesses associated with physical and psychiatric problems, and premature death. Examining social media communication about ED symptoms may provide insight into how to prevent and treat these disorders. This study is to explore topics on Twitter related to EDs. We applied the Correlation Explanation (CorEx) topic model on 18,288 ED-related tweets and identified 20 topics, which were further grouped into 8 categories. The top two topic categories are body image and ED consequences. We manually evaluated the relevance of tweets to their assigned topics and average accuracy is 77.86%. Our findings are consistent with another study using content analysis, and we identified additional topics, such as ED consequences, pornography, and treatment and education from these tweets.
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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.