Discovering the World’s Research

Ron SnyderDirector of Advanced Technology, ITHAKA/JSTOR

NASIG Annual Conference - 2012June 9, 2012

ITHAKA is a not-for-profit organization that helps the academic community use digital technologies to preserve the scholarly record and to advance research and teaching in sustainable ways.

We pursue this mission by providing innovative services that aid in the adoption of these technologies and that create lasting impact.

Who we are

JSTOR is a research platform that enables discovery, access, and preservation of scholarly content.

• Started in 1997• Journals online: 1,604• Articles online: 7.5 million• Disciplines covered: 60• Participating institutions: 7,800• Countries with participating institutions: 167

JSTOR Factoids

JSTOR site activity

User Sessions (visits)» New Sessions (per hour):

70k peak, 38k average

» Simultaneous Sessions: 44k peak, 21k average

Page Views» 3.5M per day, 6.7M peak

Content Accesses» 430k per day, 850K peak

Searches» 456k per day, 1.13M peak

• Overhaul of JSTOR Search Infrastructure• Coming Soon (Summer 2012), watch for it…

• Analytics and data warehouse• Ingesting, organizing, and analyzing billions of usage

events since JSTOR inception

• Improved external discoverability• Various SEO, Google/GS, MS-Academic projects

• Local Discovery Integration (LDI) Pilot• Machine-based document classification

ITHAKA/JSTOR Discovery Initiatives

Local Discovery Integration PilotJSTOR and Summon

» Research has shown time and again that both students and faculty are beginning their research at places other than the library OPAC, most notably Google/Google Scholar and discipline-specific electronic databases, and that the trend is continuing

Starting point for research, identified by faculty in 2003, 2006, and 2009 (2009 Faculty Study, ITHAKA)

Problem Statement:

The library build-ing

Your online library cata-

log

A general-pur-pose search

engine

A specific elec-tronic research

resource

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2003

2006

2009

Where is discovery happening?

Where JSTOR ‘sessions’ originated | Jan 2011 – Dec 2011

» As web-scale discovery services are being purchased and implemented by institutions, the value of those implementations are somewhat limited because they are (for the most part) only addressing that limited population of researchers who begin at a library-designated starting point (e.g. OPAC)

Problem Statement:

76%

16%

6%

1%

JSTOR usage | Australia | 2010 Nov.JSTOR Google/Google Scholar Known Linking Partner Library

Research Behavior: Students

Google

Library Databases

0 10 20 30 40 50 60 70

What is the easiest place to start research according to students?

Source: ProQuest survey of student research habits, 2007

Research Behavior: Faculty

The library build-ing

Your online library cata-

log

A general-pur-pose search

engine

A specific elec-tronic research

resource

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2003

2006

2009

Starting Point for Research, identified by faculty in 2003, 2006, and 2009

Source: ITHAKA 2009 Faculty Survey, 2010

» If we can more effectively reach the users at the place(s) where they normally begin their research, then we can begin to more effectively build their awareness of the resources that the institution has licensed/purchased for their purposes

» The local discovery integration (LDI) pilot study will attempt to measure changes in the student/faculty research experience by ‘embedding’ the institution’s selected web-scale discovery service in strategically-selected places in the JSTOR interface where – we believe – the user would naturally want to ‘cast a wider net’ for discovery

Concept:

2010 JSTOR Usage Highlights

Total Significant Accesses 594,888,001

Articles Downloaded 74,901,344

Articles Viewed 112,751,906

Searches Performed 168,720,887

Inbound Links from Licensed Partners 13,013,904

Inbound Links from Google/Scholar 157,903,053

How it works

We placed links at various places along the research workflow in JSTOR to allow students and researchers to “Cast a wider net”

Links Out

• Search Results Advanced Search Page Search Results View

• 3rd Page “Lightbox” pop-up• Article View - Incoming from Google

• Article View - All other non-Google

• Zero Results Page

» JSTOR may not be the most appropriate starting place in every instance, but it is a trusted and familiar interface. This will allow the user to ‘flowback’ to another starting place (e.g. the library)

Search results page

• Uses the familiar university logo to grab attention

• Inserts search terms into link text to notify user of customized behavior

• Positioned proximate to search results; relevant during the search result evaluation phase

» In this instance, the user has found nothing and the most typical web response is to hit the ‘Back’ button. If we allow the user – at this point – to execute a search in the local discovery interface, we might improve the user experience

Empty results page

• One of the key places where a user is likely to want to try a different, broader search

• Larger placement takes advantage of available real estate and cognitive space

• Users typically do not spend time on this page so it is important to increase notice-ability and self-explanation

» In 2010, over 32M Google/Google Scholar searches brought users directly to an article page. They may or may not have found what they really wanted, so we’d like to give them an alternative discovery choice

Article page after Google search

• Visible when coming from a Google or Google Scholar search

• Captures basic search terms from the search

• Provides an opportunity to convert a user from a Google/Google Scholar user to a Summon user

» In 2010, almost 113M articles were viewed in JSTOR. Again, they may not have found what they really wanted, so we’d like to give them an alternative discovery choice

Article page after JSTOR search

• Visible when coming from a JSTOR search

• Raises visibility of the feature by exposing it to a large number of users

• Inserts search terms into link text to notify user of customized behavior

Results View: All Pages

Link out from the bottom of all pages of the search results view. This will allow more opportunities to link out for students/ researchers combing through large sets of results.

Results View: 3rd Page

Pop-up on the third page of search results Prompts the student/ researcher to indicate whether they wish to link out through the LDI. This will enable us to measure whether students wish to “cast a wider net” or not. In the other link scenarios we don’t have a baseline of how many students do not notice the link vs. choose not to use it

Link out to Discovery Platform

Results Overview

Data shown is for all institutions participating in Summon LDIDate range: July 2011 – February 2012

» Highest usage occurred in Zero Results scenario

Machine-Based Article ClassifierAssigning Articles to Disciplines

JSTOR Corpus• 60 disciplines• 1,600 journals• Nearly 8 million articles

• Disciplines are associated at the Journal level• All articles in a Journal inherit the Journal assigned

disciplines• Using this approach many articles have incomplete

and/or incorrect discipline tagging hindering discovery

• How to assign disciplines to articles?

The Problem

• Human classification and tagging is not feasible• A machine-based classification process is desired

• Topic models are a way of finding structure in a set of documents

• They allow is to find “latent” themes• A topic model is not a topic map• Some topic modeling approaches include

• Latent Semantic Analysis (LSI/LSA) (Deerwester 1990)• Probabilistic LSA (Hoffmann 1999)• Latent Dirichlet Allocation (LDA) (Blei 2003)

Topic Models

LDA – Latent Dirichlet Allocation• A generative probabilistic model for analyzing

collections of documents• A Bayesian model where each document is modeled

as a mixture of topics (disciplines)• Models semantic relationships between documents

based on word co-occurrences

Topic Modeling – our approach

• We select the most representative documents from each JSTOR discipline to build a topic model (from the vocabulary of the document sample)• This sampling and vocabulary modeling is the most important part

of the process!• We’re still experimenting with this, but find the citation network

provides a good means for identifying core documents in a discipline

• Also considering whether usage data might be leveraged here

• Each document in the corpus is then analyzed and compared to the topic model to determine how well it matches each topic• A probability distribution is generated providing discipline weights• The top weighted discipline(s) are associated with each article

The Process

• On-site discovery• Will be a key element of our overhauled search infrastructure,

tentatively scheduled for beta release mid-summer

• Use in article-level discipline/subject/topic mappings for better integration with aggregated indexes• Will support a richer data feed for Summon, for instance

Application