Twitter for health - Building a social media search engine to better understand and curate laypersons’ personal experiences

Hanna Suominen, Leif Hanlen, Cécile Paris

Research output: A Conference proceeding or a Chapter in BookChapter

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

Healthcare professionals, trainees, and laypersons increasingly use social media over the Internet. As a result, the value of such platforms as a vital source of health information is widely acknowledged. These technologies bring a new dimension to health care by offering a communication medium for patients and professionals to interact, share, and survey information as well as support each other emotionally during an illness. Such active online discussions may also help in realizing the collective goal of improving healthcare outcomes and policies. However, in spite of the advantages of using social media as a vital communication medium for those seeking health information and for those studying social trends based on patient blog postings, this new medium of digital communication has its limitations too. Namely, the current inability to access and curate relevant information in the ever-increasing gamut of messages. In this chapter, we are seeking to understand and curate laypersons’ personal experiences on Twitter. To do so, we propose some solutions to improve search, summarization, and visualization capabilities for Twitter (or social media in general), in both real time and retrospectively. In essence, we provide a basic recipe for building a search engine for social media and then make it increasingly more intelligent through smarter processing and personalization of search queries, tweet messages, and search results. In addition, we address the summarization aspect by visualizing topical clusters in tweets and further classifying the retrieval results into topical categories that serve professionals in their work. Finally, we discuss information curation by automating the classification of the information sources as well as combining, comparing, and correlating tweets with other sources of health information. In discussing all these important features of social media search engines, we present systems, which we ourselves have developed that help to identify useful information in social media.

Original languageEnglish
Title of host publicationText Mining of Web-Based Medical Content
EditorsAmy Neustein
Place of PublicationBerlin
PublisherWalter de Gruyter
Pages133-173
Number of pages41
ISBN (Electronic)9781614513902
ISBN (Print)9781614515418
DOIs
Publication statusPublished - 1 Jan 2014

Fingerprint

layperson
twitter
Search engines
social media
search engine
Health
Communication
health information
health
communication medium
experience
Blogs
Health care
Visualization
Internet
personalization
Processing
weblog
trainee
visualization

Cite this

Suominen, H., Hanlen, L., & Paris, C. (2014). Twitter for health - Building a social media search engine to better understand and curate laypersons’ personal experiences. In A. Neustein (Ed.), Text Mining of Web-Based Medical Content (pp. 133-173). Berlin: Walter de Gruyter. https://doi.org/10.1515/9781614513902.133
Suominen, Hanna ; Hanlen, Leif ; Paris, Cécile. / Twitter for health - Building a social media search engine to better understand and curate laypersons’ personal experiences. Text Mining of Web-Based Medical Content. editor / Amy Neustein. Berlin : Walter de Gruyter, 2014. pp. 133-173
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Suominen, H, Hanlen, L & Paris, C 2014, Twitter for health - Building a social media search engine to better understand and curate laypersons’ personal experiences. in A Neustein (ed.), Text Mining of Web-Based Medical Content. Walter de Gruyter, Berlin, pp. 133-173. https://doi.org/10.1515/9781614513902.133

Twitter for health - Building a social media search engine to better understand and curate laypersons’ personal experiences. / Suominen, Hanna; Hanlen, Leif; Paris, Cécile.

Text Mining of Web-Based Medical Content. ed. / Amy Neustein. Berlin : Walter de Gruyter, 2014. p. 133-173.

Research output: A Conference proceeding or a Chapter in BookChapter

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AB - Healthcare professionals, trainees, and laypersons increasingly use social media over the Internet. As a result, the value of such platforms as a vital source of health information is widely acknowledged. These technologies bring a new dimension to health care by offering a communication medium for patients and professionals to interact, share, and survey information as well as support each other emotionally during an illness. Such active online discussions may also help in realizing the collective goal of improving healthcare outcomes and policies. However, in spite of the advantages of using social media as a vital communication medium for those seeking health information and for those studying social trends based on patient blog postings, this new medium of digital communication has its limitations too. Namely, the current inability to access and curate relevant information in the ever-increasing gamut of messages. In this chapter, we are seeking to understand and curate laypersons’ personal experiences on Twitter. To do so, we propose some solutions to improve search, summarization, and visualization capabilities for Twitter (or social media in general), in both real time and retrospectively. In essence, we provide a basic recipe for building a search engine for social media and then make it increasingly more intelligent through smarter processing and personalization of search queries, tweet messages, and search results. In addition, we address the summarization aspect by visualizing topical clusters in tweets and further classifying the retrieval results into topical categories that serve professionals in their work. Finally, we discuss information curation by automating the classification of the information sources as well as combining, comparing, and correlating tweets with other sources of health information. In discussing all these important features of social media search engines, we present systems, which we ourselves have developed that help to identify useful information in social media.

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Suominen H, Hanlen L, Paris C. Twitter for health - Building a social media search engine to better understand and curate laypersons’ personal experiences. In Neustein A, editor, Text Mining of Web-Based Medical Content. Berlin: Walter de Gruyter. 2014. p. 133-173 https://doi.org/10.1515/9781614513902.133