DIGGING INTO DATAA presentation to the NSF

Cascades, Islands, or Streams? Time, Topic and Scholarly Activities in Humanities and Social Science

Research

DIGGING INTO DATAWho is

LARIVIÈRE

MILOJEVIĆSUGIMOTO DINGTHELWALL HOLMBERG

DIGGING INTO DATAWhat

DIGGING INTO DATAWhat

Time: 1743-2011Dissertations: 2,307,555Subjects: 166Schools: 1,490Countries: 66

DIGGING INTO DATAWhat

Time: 1900-2011Medicine Articles: 14,698,810Medicine References: 380,058,817Social Science Articles: 4,228,702Social Science References: 77,908,552Arts & Humanities Articles: 3,151,986Arts & Humanities References: 26,180,296Natural Science Articles: 14,853,029Natural Science References: 335,144,498

DIGGING INTO DATAWhat

Time: 2007-2012Articles: 744,584Broad Subject areas: 7Matching ISI records: ~50%

DIGGING INTO DATAWhat

Time: 2010-currentTweets: 100,000 per monthSubjects: 11Generalist journals: 4Scientists and science journalists: 350

DIGGING INTO DATAWhat

Time: 2006-2012Videos: 1,202Views on TED: 620,406,446Views on YouTube: 111,681,275Comments on YouTube: 414,311

DIGGING INTO DATAWhy are we

Integrate several datasets representing a broad range of scholarly activities

Use methodological and data triangulation to explore the lifecycle of topics within and across a range of scholarly activities

Develop transparent tools and techniques to enable future predictive analyses

DIGGING INTO DATAShow me the

Domain # Papers Aver. DelayEarth and Space 52438 0.40Physics 176557 0.57Biomedical Research 3119 0.57Chemistry 1664 0.61Engineering and Technology 8020 1.04Biology 285 1.18Mathematics 44535 1.44Social Sciences 367 1.61Professional Fields 382 1.77Clinical Medicine 566 1.93Psychology 80 2.26Humanities 111 3.88Health 37 4.54Arts 36 4.69All disciplines 288200 0.69

DIGGING INTO DATAShow me the

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

0

0.5

1

1.5

2

2.5

3

3.5

ArXiv and WoS (WoS version)

Only in WoS

ArXiv and WoS (ArXiv version)

ArXiv Only

Me

an

cia

tio

n r

ate

DIGGING INTO DATAShow me the

DIGGING INTO DATAShow me the

H=Hedges: lowered certainty (“perhaps”)B=Boosters: heightened certainty (“absolutely”)SM=Self-mentions: self-references (“the author”)AM=Attitude markers: author-text positions (“admitedly”)EM=Engagement markers: reader positions (“should”)

DIGGING INTO DATAShow me the

Metric Minimum Median Mean Maximum Total ValidTED web site views 44,441 338,969 517,437 9,946,996 620,406,446 1,199YouTube views 462 43,311 99,184 3,991,983 111,681,275 1,126Blog citations 0 3,120 9,073 441,000 10,905,376 1,202YouTube Likes 2 485 900 26,591 1,013,231 1,126YouTube Favorite count 3 299 767 38,139 863,458 1,126YouTube comments 0 195 368 21,703 414,311 1,126TED web site comments 8 117 187 5,921 224,629 1,199YouTube Dislikes 0 34 69 1,456 78,053 1,126Academic syllabi 0 1 2 50 2,070 1,202PDF and Word documents 0 0 0 49 592 1,202Google Scholar citations 0 0 0 75 505 1,202Google Books citations 0 0 0 18 434 1,202PowerPoint presentations 0 0 0 238 392 1,202Mendeley readers 0 0 0 30 231 1,202Web of Knowledge citations 0 0 0 5 47 1,202YouTube Like proportion 0.260 0.941 0.900 1.000 - 1,126

DIGGING INTO DATAShow me the

Metric Academic Non-academics TED web site views 327,904 321,320YouTube views 49,660 45,414Blog citations 2,340 2,246YouTube comments 223 190TED web site comments 111 112Online mentions related to academic syllabi 1 1Online mentions in PDF and Word documents (acad. higher) 0 0*Google Scholar citations 0 0Google Books citations 0 0Online mentions in PowerPoint presentations 0 0Mendeley readers 0 0Web of Knowledge citations 0 0YouTube Like proportion 0.9574 0.9271**

DIGGING INTO DATAKeep on

DIGGING INTO DATAKeep on

DIGGING INTO DATAComments

DIGGING INTO DATAAnalyzing sentiment

We are developing sentiment analysis software SentiStrength for the texts in the project

The program will classify the sentiment of texts based upon lexicons of words – e.g., good, bad – plus special rules for negation, booster words (e.g., very) etc.

The lexicon will be customised for different genres – e.g., flawed, incomplete for academic texts, dull, inspiring for videos

DIGGING INTO DATALead-lag analysis

DIGGING INTO DATAAfter

Scott Weingart

DIGGING INTO DATATowards a new model

Draft

Report

Conf. paper

ArticleReview

Book

Tweet

Email

Blog

Slid

esho

w

Multim

edia

Cura

ted D

BProducer

Consumer

Prosumer

DIGGING INTO DATAQuestions about

Cassidy R. Sugimoto (PI)Assistant Professor

School of Library and Information ScienceIndiana University Bloomington

sugimoto@indiana.edu