flying to the top, one tweet at a time: using social media to rank online search results robyn b....
Post on 19-Dec-2015
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![Page 1: 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](https://reader030.vdocuments.site/reader030/viewer/2022032703/56649d2e5503460f94a0576e/html5/thumbnails/1.jpg)
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 SystemUniversity of Pittsburgh
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Workshops
Website
Software Licensing
Consultations
Molecular Biology Information Service
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Online Bioinformatics Resources Collection (OBRC)
http://www.hsls.pitt.edu/obrc/
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Resources displayed by keyword ranking
http://www.hsls.pitt.edu/obrc/
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Challenges:
Many tools exist and increasing in number
User may retrieve several resources
Common question –How do I know which one(s) to
use?
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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
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Why use the social media??
• No official rankings of bioinformatics tools
• Opinions of several people
• Social media data has many applications
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http://beta.socialguide.com/
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Methodology
Wrote 5 research questions
Common bioinformatics queries
Each question listed 3 possible resources to accomplish that task
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Resources wereranked using social media data
Experts (2) independently ranked resources
Methodology
Research questions
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Methodology – Social Media Ranking
Sources used for data collection
Google Blogs Google Discussions
Google Discussions includes• Forums• Groups• Comments
www.google.com
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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
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Methodology – Social Media Ranking
Searched “all time”
Optimized for most accurate retrieval
• Resource in quotes• Increased specificity, decreased noise• Fewer hits
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[(“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)]
• Put all OBRC resources in bioinformatics context• Automate the searches
Example of search of UCSC genome browser
Methodology – Search Filter
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Results Bioinformatics Tools
Blogs + Discussion Raw
NumbersSocial
Media RankExpert 1 Rank
Expert 2 Rank
CPHmodels 49 2 2 2
3-D protein prediction ESypred3D 17 3 3 3
SWISS-MODEL 228 1 1 1
IDT SciTools 4 2 2 2
PCR primer design Primer3 728 1 1 1
Primer Design Assistant 0 3 3 3
DIANA-microT 12 1 1 2
microRNA target design miRGator 9 2 2 3
siRNA target finder Ambion 3 3 3 1
ClustalW 1494 1 1 3multiple sequence alignment
ECR Browser 8 3 3 1
Tcoffee 63 2 2 2
Ensembl 3070 1 3 2
genome browsers NCBI Map Viewer 56 3 2 3
UCSC Genome Browser 928 2 1 1
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Bioinformatics Tools
Blogs + Discussion Raw
NumbersSocial
Media RankExpert 1 Rank
Expert 2 Rank
CPHmodels 49 2 2 2
3-D protein prediction ESypred3D 17 3 3 3
SWISS-MODEL 228 1 1 1
IDT SciTools 4 2 2 2
PCR primer design Primer3 728 1 1 1
Primer Design Assistant 0 3 3 3
DIANA-microT 12 1 1 2
microRNA target design miRGator 9 2 2 3
siRNA target finder Ambion 3 3 3 1
ClustalW 1494 1 1 3multiple sequence alignment
ECR Browser 8 3 3 1
Tcoffee 63 2 2 2
Ensembl 3070 1 3 2
genome browsers NCBI Map Viewer 56 3 2 3
UCSC Genome Browser 928 2 1 1
Results
![Page 17: 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](https://reader030.vdocuments.site/reader030/viewer/2022032703/56649d2e5503460f94a0576e/html5/thumbnails/17.jpg)
Bioinformatics Tools
Blogs + Discussion Raw
NumbersSocial
Media RankExpert 1 Rank
Expert 2 Rank
CPHmodels 49 2 2 2
3-D protein prediction ESypred3D 17 3 3 3
SWISS-MODEL 228 1 1 1
IDT SciTools 4 2 2 2
PCR primer design Primer3 728 1 1 1
Primer Design Assistant 0 3 3 3
DIANA-microT 12 1 1 2
microRNA target design miRGator 9 2 2 3
siRNA target finder Ambion 3 3 3 1
ClustalW 1494 1 1 3multiple sequence alignment
ECR Browser 8 3 3 1
Tcoffee 63 2 2 2
Ensembl 3070 1 3 2
genome browsers NCBI Map Viewer 56 3 2 3
UCSC Genome Browser 928 2 1 1
Results
![Page 18: 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](https://reader030.vdocuments.site/reader030/viewer/2022032703/56649d2e5503460f94a0576e/html5/thumbnails/18.jpg)
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
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Limitations
• Quotation marks can be limiting ifresource >1 word
• Very small part of the total social media
• “Negative” discussion about a resource
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Future Directions
• Test > 3 bioinformatics tools/category
• Increase number of expert ratings
• Test applicability of system in areas other than bioinformatics
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Special thanks to:
Project collaborators and experts:Ansuman Chattopadhyay, PhDCarrie Iwema, PhD, MLS
Research and academic advisors: Nancy Tannery, MLSRebecca Crowley, MD, MS
Funding from the Pittsburgh Biomedical Informatics Training Program
NLM Grant 3 T15 LM007059-23S1