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Enhancing Bioinformatics Resource Ranking through Social Media Insights

This study explores the innovative use of social media to rank bioinformatics resources. Given the plethora of available tools and the absence of official rankings, this research aims to develop a method for assessing the most reputable resources. By utilizing social media data and expert opinions, we create a ranking system that reflects current trends in bioinformatics. Our methodology includes analyzing discussions, blogs, and expert ratings to provide patrons with a curated starting point for accessing bioinformatics tools, addressing significant challenges faced by users.

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Enhancing Bioinformatics Resource Ranking through Social Media Insights

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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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