Flying to the Top, One Tweet at a - University of Pittsburgh

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


Workshops

Website

Software
Licensing

Consultations

Molecular Biology Information Service

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

Resources were

ranked using social

media data

Experts (2)
independently

ranked resources

Methodology

Research questions

Methodology
–

Social Media Ranking

Sources used for data collection




Google Blogs



Google Discussions




Google Discussions includes

•

Forums

•

Groups

•

Comments

www.google.com





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

Methodology
–

Social Media Ranking

Searched “all time”


Optimized for most accurate retrieval


•

Resource in quotes

•

Increased specificity, decreased noise

•

Fewer hits


[(“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

Results




Bioinformatics Tools

Blogs + Discussion
Raw Numbers

Social Media
Rank

Expert 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

3

multiple 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




Bioinformatics Tools

Blogs + Discussion
Raw Numbers

Social Media
Rank

Expert 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

3

multiple 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




Bioinformatics Tools

Blogs + Discussion
Raw Numbers

Social Media
Rank

Expert 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

3

multiple 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


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