A framework to extract biomedical knowledge from gluten-related tweets: The case of dietary concerns in digital era

作者：

Highlights：

• A methodology to process, classify, visualise and analyse the in social media knowledge for Health Informatics.

• The case study is the analysis of the gluten-free food community on Twitter.

• Characterise the different social media accounts involved in the public discussion in terms of role, gender and geo-location.

• Ontology engineering, named entity recognition, machine learning and graph mining techniques are applied.

• Results helps to identify alimentary risk, market opportunities and demographic patterns to improve the awareness campaigns.

摘要

•A methodology to process, classify, visualise and analyse the in social media knowledge for Health Informatics.•The case study is the analysis of the gluten-free food community on Twitter.•Characterise the different social media accounts involved in the public discussion in terms of role, gender and geo-location.•Ontology engineering, named entity recognition, machine learning and graph mining techniques are applied.•Results helps to identify alimentary risk, market opportunities and demographic patterns to improve the awareness campaigns.

论文关键词：Social media,Sociome profiling,Text mining,Graph mining,Machine learning,Health for informatics

论文评审过程：Received 4 September 2020, Revised 27 April 2021, Accepted 22 June 2021, Available online 25 June 2021, Version of Record 2 July 2021.

论文官网地址：https://doi.org/10.1016/j.artmed.2021.102131