  Twitter messages (tweets) contain various types of information, which include
health-related information. Analysis of health-related tweets would help us
understand health conditions and concerns encountered in our daily life. In
this work, we evaluated an approach to extracting causal relations from tweets
using natural language processing (NLP) techniques. We focused on three
health-related topics: stress", "insomnia", and "headache". We proposed a set
of lexico-syntactic patterns based on dependency parser outputs to extract
causal information. A large dataset consisting of 24 million tweets were used.
The results show that our approach achieved an average precision between 74.59%
and 92.27%. Analysis of extracted relations revealed interesting findings about
health-related in Twitter.
