Journal Article

Extracting health-related causality from twitter messages using natural language processing

Doan, Son, Li, Peter W., Torii, Manabu, Tilak, Sameer S., Yang, Elly W., Zisook, Daniel S.

2019

Overview

Background Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques. Methods Lexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: “stress”, “insomnia”, and “headache.” A large dataset consisting of 24 million tweets are used. Results The results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations. Conclusions Manual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Annotations / Artificial intelligence / Automation / Causal relationships / Causality / Cause-effect / Datasets as Topic / Dependence / Headache / Health / Health Informatics / Humans / Information services / Information Storage and Retrieval / Information Systems and Communication Service / Insomnia / Language / Linguistics / Management of Computing and Information Systems / Media coverage / Medicine / Medicine & Public Health / Mental depression / Messages / Multilingualism / Natural Language Processing / Natural language processing (NLP) / Semantics / Sleep disorders / Sleep Initiation and Maintenance Disorders / Social Media / Social networks / Stress, Psychological / Technology application / Text Messaging / Twitter