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

BMC Med Inform Decis Mak. 2019 Apr 4;19(Suppl 3):79. doi: 10.1186/s12911-019-0785-0.

Abstract

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.

Keywords: Causal relationships; Causality; Cause-effect; Natural language processing (NLP); Twitter.

MeSH terms

  • Causality*
  • Datasets as Topic
  • Headache
  • Humans
  • Information Storage and Retrieval*
  • Natural Language Processing*
  • Sleep Initiation and Maintenance Disorders
  • Social Media
  • Stress, Psychological
  • Text Messaging*