ArticleJournal of medical Internet research2019
Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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.
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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.
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Who cites it
15 citing papers in PubMed.
- Multilingual Disparities in Large Language Model-Based Symptom Detection for Global Disease Surveillance: Evaluation Study.JMIR AI · 2026Article
- Using the Natural Language Processing System Medical Named Entity Recognition-Japanese to Analyze Pharmaceutical Care Records: Natural Language Processing Analysis.JMIR formative research · 2024Article
- Use of Social Media for Health Advocacy for Digital Communities: Descriptive Study.JMIR formative research · 2023Article
- Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study.JMIR formative research · 2023Article
- Development of a novel drug information provision system for Kampo medicine using natural language processing technology.BMC medical informatics and decision making · 2023Article
- Impact of #PsychTwitter in promoting global psychiatry: A hashtag analysis study.Frontiers in public health · 2023Article
- Using Twitter to understand perspectives and experiences of dementia and caregiving at the beginning of the COVID-19 pandemic.Dementia (London, England) · 2022Article
- A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness.JMIR medical informatics · 2021Article
- Analysis of Media Outlets on Women's Health: Thematic and Quantitative Analyses Using Twitter.Frontiers in public health · 2021Article
- COVID-19: Detecting Government Pandemic Measures and Public Concerns from Twitter Arabic Data Using Distributed Machine Learning.International journal of environmental research and public health · 2021Article
- A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums.Journal of medical systems · 2020Article
- Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study.JMIR public health and surveillance · 2019Article
- Exploring the Extent of the Hikikomori Phenomenon on Twitter: Mixed Methods Study of Western Language Tweets.Journal of medical Internet research · 2019Article
- Areas of Interest and Stigmatic Attitudes of the General Public in Five Relevant Medical Conditions: Thematic and Quantitative Analysis Using Twitter.Journal of medical Internet research · 2019Article
- Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations.Journal of medical Internet research · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media-based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient's symptom and those that do not.
objectiveThis study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP.
methodsIn summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss.
resultsThe best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively.
conclusionsThis paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications.
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