Crawling credible online medical sentiments for social intelligence

Ahmed Abbasi, Tianjun Fu, Daniel Zeng, Donald Adjeroh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Social intelligence derived from Health 2.0 content has become of significant importance for various applications, including post-marketing drug surveillance, competitive intelligence, and to assess health-related opinions and sentiments. However, the volume, velocity, variety, and quality of online health information present challenges, necessitating enhanced facilitation mechanisms for medical social computing. In this study, we propose a focused crawler for online medical content. The crawler leverages enhanced credibility and context information. An extensive evaluation was performed against several comparison methods, on an online Health 2.0 test bed encompassing millions of pages. The results revealed that the proposed method was able to collect relevant content with considerably higher precision and recall rates than comparison methods, on content associated with medical websites, forums, blogs, and social networking sites. Furthermore, an example was used to illustrate the usefulness of the crawler for accurately representing online drug sentiments. Overall, the results have important implications for social computing, where a high-quality data and information foundation are imperative to the success of any overlying social intelligence initiative.

Original languageEnglish (US)
Title of host publicationProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013
Pages254-263
Number of pages10
DOIs
StatePublished - Dec 1 2013
Event2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013 - Washington, DC, United States
Duration: Sep 8 2013Sep 14 2013

Publication series

NameProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013

Other

Other2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013
CountryUnited States
CityWashington, DC
Period9/8/139/14/13

Keywords

  • Focused crawling
  • Sentiment analysis
  • Social intelligence
  • Social media analytics
  • Text mining
  • Web mining

ASJC Scopus subject areas

  • Software

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  • Cite this

    Abbasi, A., Fu, T., Zeng, D., & Adjeroh, D. (2013). Crawling credible online medical sentiments for social intelligence. In Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013 (pp. 254-263). [6693340] (Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013). https://doi.org/10.1109/SocialCom.2013.43