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Molecular Biology Information Service

Flying to the Top, One Tweet at a Time:  Using Social Media to Rank Online Search Results Robyn B. Reed, MA, MLIS Co-authors: Carrie L. Iwema, PhD, MLS Ansuman Chattopadhyay, PhD Health Sciences Library System University of Pittsburgh. Molecular Biology Information Service.

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Molecular Biology Information Service

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  1. Flying to the Top, One Tweet at a Time:  Using Social Media to Rank Online Search ResultsRobyn B. Reed, MA, MLISCo-authors: Carrie L. Iwema, PhD, MLS Ansuman Chattopadhyay, PhDHealth Sciences Library SystemUniversity of Pittsburgh

  2. Molecular Biology Information Service

  3. Online Bioinformatics Resources Collection (OBRC) http://www.hsls.pitt.edu/obrc/

  4. Resources displayed by keyword ranking http://www.hsls.pitt.edu/obrc/

  5. Challenges: Many tools exist and increasing in number User may retrieve several resources Common question – How do I know which one(s) to use?

  6. Goal: Provide up-to-date ratings of most highly regarded resources in bioinformatics Objectives: Using social media, design ranking system of OBRC resources Determine if social media results reflect opinions of bioinformatics experts

  7. Why use the social media?? • No official rankings of bioinformatics tools • Opinions of several people • Social media data has many applications

  8. http://beta.socialguide.com/

  9. Methodology Wrote 5 research questions Common bioinformatics queries Each question listed 3 possible resources to accomplish that task

  10. Methodology Research questions Resources were ranked using social media data Experts (2) independently ranked resources

  11. Methodology – Social Media Ranking • Sources used for data collection • Google Blogs • Google Discussions • Google Discussions includes • Forums • Groups • Comments www.google.com

  12. Twitter considered and removed • 50% of the resources had zero Tweets • 20% captured non-specific Tweets • Facebook not included • Concern over private settings Methodology – Data Sources

  13. Methodology – Social Media Ranking • Searched “all time” • Optimized for most accurate retrieval • Resource in quotes • Increased specificity, decreased noise • Fewer hits

  14. Methodology – Search Filter • Put all OBRC resources in bioinformatics context • Automate the searches [(“ucsc genome browser”) AND ( bioinformatics | genome | genetics | genomics | computer | algorithm | software | server | database | computer model | protein | proteomics | proteome | gene | DNA | RNA | sequence | alignment | interactions | structure | modeling | prediction | biochemistry | molecular biology | systems biology | computational biology)] Example of search of UCSC genome browser

  15. Results

  16. Results

  17. Results

  18. Conclusions: • This system can be used to determine highly regarded tools • Explain that rankings are subjective; • try the top 3-5 resources • Provides patron with a starting point when using the OBRC

  19. Limitations • Quotation marks can be limiting if • resource >1 word • Very small part of the total social media • “Negative” discussion about a resource

  20. Future Directions • Test > 3 bioinformatics tools/category • Increase number of expert ratings • Test applicability of system in areas other than bioinformatics

  21. Special thanks to: Project collaborators and experts: Ansuman Chattopadhyay, PhD Carrie Iwema, PhD, MLS Research and academic advisors: Nancy Tannery, MLS Rebecca Crowley, MD, MS Funding from the Pittsburgh Biomedical Informatics Training Program NLM Grant 3 T15 LM007059-23S1

  22. Thank you! Any questions? Robyn Reed rreed@pitt.edu

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