Title :
Health news feed: Identifying personally relevant health-related URLs in tweets
Author :
Steele, Robert ; Min, Kyongho
Author_Institution :
Discipline of Health Inf., Univ. of Sydney, Sydney, NSW, Australia
Abstract :
A common use of micro-blogging systems, such as Twitter, is to `tweet´ or `re-tweet´ URLs of the latest news articles. The challenge is that with the large number of such micro-blog posts, it is difficult to find and filter to just the most relevant news for an individual. In this paper, we propose and detail the health news feed system which utilises a three-stage filtering and categorisation process with three types of knowledge resources using natural language processing (NLP) technologies for filtering and extracting personally-relevant health-related news articles referred to in tweets. The three stages are term-based filtering, content filtering, and categorization.
Keywords :
content-based retrieval; information filtering; medical information systems; natural language processing; social networking (online); NLP technologies; Twitter; URL retweeting; categorisation process; knowledge resources; microblog posts; natural language processing technologies; personally relevant health-related URL identification; personally-relevant health-related news article extraction; personally-relevant health-related news article filtering; term-based filtering; three-stage content filtering process; Cancer; Feeds; Information filtering; Media; Thesauri; Twitter; categorisation; health social media; micro-blog filtering; mobile health; personalised filtering;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2012 7th IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-2118-2
DOI :
10.1109/ICIEA.2012.6360778
