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
Workshops
Consultations
Website
Software
Licensing
Online Bioinformatics Resources Collection
(OBRC)
http://www.hsls.pitt.edu/obrc/
Resources displayed by keyword ranking
http://www.hsls.pitt.edu/obrc/
Challenges:
Many tools exist and increasing in number
User may retrieve several resources
Common question –
How do I know which one(s) to use?
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
Why use the social media??
• No official rankings of bioinformatics tools
• Opinions of several people
• Social media data has many applications
http://beta.socialguide.com/
Methodology
Wrote 5 research questions
Common bioinformatics queries
Each question listed 3 possible resources
to accomplish that task
Methodology
Research questions
Experts (2)
independently
ranked resources
Resources were
ranked using social
media data
Methodology – Social Media Ranking
Sources used for data collection
 Google Blogs
 Google Discussions
Google Discussions includes
• Forums
• Groups
• Comments
www.google.com
Methodology – Data Sources
Twitter considered and removed
• 50% of the resources had zero Tweets
• 20% captured non-specific Tweets
Facebook not included
• Concern over private settings
Methodology – Social Media Ranking
Searched “all time”
Optimized for most accurate retrieval
• Resource in quotes
• Increased specificity, decreased noise
• Fewer hits
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
Results
Bioinformatics Tools
CPHmodels
Blogs + Discussion
Raw Numbers
Social Media Expert 1 Expert 2
Rank
Rank
Rank
49
2
2
2
17
3
3
3
228
1
1
1
4
2
2
2
728
1
1
1
Primer Design Assistant
0
3
3
3
DIANA-microT
12
1
1
2
9
2
2
3
3
3
3
1
1494
1
1
3
8
3
3
1
63
2
2
2
3070
1
3
2
56
3
2
3
928
2
1
1
3-D protein prediction ESypred3D
SWISS-MODEL
IDT SciTools
PCR primer design Primer3
microRNA target design miRGator
siRNA target finder Ambion
ClustalW
multiple sequence alignment ECR Browser
Tcoffee
Ensembl
genome browsers NCBI Map Viewer
UCSC Genome Browser
Results
Bioinformatics Tools
CPHmodels
Blogs + Discussion
Raw Numbers
Social Media Expert 1 Expert 2
Rank
Rank
Rank
49
2
2
2
17
3
3
3
228
1
1
1
4
2
2
2
728
1
1
1
Primer Design Assistant
0
3
3
3
DIANA-microT
12
1
1
2
9
2
2
3
3
3
3
1
1494
1
1
3
8
3
3
1
63
2
2
2
3070
1
3
2
56
3
2
3
928
2
1
1
3-D protein prediction ESypred3D
SWISS-MODEL
IDT SciTools
PCR primer design Primer3
microRNA target design miRGator
siRNA target finder Ambion
ClustalW
multiple sequence alignment ECR Browser
Tcoffee
Ensembl
genome browsers NCBI Map Viewer
UCSC Genome Browser
Results
Bioinformatics Tools
CPHmodels
Blogs + Discussion
Raw Numbers
Social Media Expert 1 Expert 2
Rank
Rank
Rank
49
2
2
2
17
3
3
3
228
1
1
1
4
2
2
2
728
1
1
1
Primer Design Assistant
0
3
3
3
DIANA-microT
12
1
1
2
9
2
2
3
3
3
3
1
1494
1
1
3
8
3
3
1
63
2
2
2
3070
1
3
2
56
3
2
3
928
2
1
1
3-D protein prediction ESypred3D
SWISS-MODEL
IDT SciTools
PCR primer design Primer3
microRNA target design miRGator
siRNA target finder Ambion
ClustalW
multiple sequence alignment ECR Browser
Tcoffee
Ensembl
genome browsers NCBI Map Viewer
UCSC Genome Browser
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
Limitations
• Quotation marks can be limiting if
resource >1 word
• Very small part of the total social media
• “Negative” discussion about a resource
Future Directions
• Test > 3 bioinformatics tools/category
• Increase number of expert ratings
• Test applicability of system in areas other
than bioinformatics
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
Thank you!
Any questions?
Robyn Reed
rreed@pitt.edu