Transcript Modeling Complex Systems - University of Texas at Austin

Informetrics & IR
• Presentation
• Readings Discussion & Review
• Projects & Papers
Why use metrics?
- Apply theory from another field to solve IS problems
- We need new modeling techniques or metaphors to
examine these complex systems
- An attempt to apply some new models and
metaphors to complex systems
• Bibliometrics
-
Direct Citation Counting
Bib Coupling
Co-Citation Analysis
Bibliometric Laws
- Web Servers
- Server Log
- Log Analysis
How do Informetrics impact IR?
• Measures of:
- Content & subject area
- Relationships
- Use & popularity
• An information-based view of communications,
focused on documents
- Instead of the text in a document, focus on the document
properties (metadata?)
• Author(s)
• Dates
• Publication source(s)
• Front Matter: Titles & Contact info
• Back Matter: Citations & Support
What are these metrics?
- Bibliometrics
• “series of techniques that seek to quantify the
process of written communication.“ Ikpaahindi
• counting and analyzing citations
• consistently observable patterns
• referenced in key places: Science Citation Index,
Social Science Citation Index, Arts and
Humanities Citation Index
- Webometrics
• Applying bibliometric methods to Web pages &
Web sites
- Informetrics
• Wider scale application of methods to
networked information sources
Citing & Linking
-
paying homage to pioneers
giving credit for related work (homage to peers)
identifying methodology, equipment, etc.
background reading
correcting one’s own work
correcting the work of others
criticizing previous work
substantiating claims
alerting to forthcoming work
providing leads to poorly disseminated, poorly indexed, or un-cited
work
authenticating data and classes of fact - physical constants, etc.
identifying original pubs in which an idea or concept was discussed
id original pub or other work describing an eponymic concept or
term (Hodgkin’s Disease)
disclaiming work or ideas of others (negative claims)
disputing priority claims of others (negative homage)
Direct Citation Counting
- How many citations over a given period of time.
- Impact formula:
• n journal citations/n citable articles published
- Immediacy index:
• n citations received by article during the year/
total number of citable articles published
Bibliometric Coupling
- “a number of papers bear a
meaningful relation to each other
when they have one or more
references in common” Kessler
- What’s the Web equivalent?
Co-Citation Analysis
- if two references are cited together, in a latter
literature, the two references are themselves
related. the greater the number of times they are
cited together, the greater their cocitation strength.
(Marshakova and Small 1973 independently)
- How about Web citations?
- What’s a set of Web pages? A Site, a long page?
Finer Points
• Classification of references:
• is the reference conceptual or operational
• is the reference organic or perfunctory
• is the reference evolutionary or juxtapositional
(built on a preceding or an alternative to it)
• is the reference confirmative or negational
• Citation reference errors:
- multiple authors (not primary or “et. al.) what
contribution/influence by order of names?
- self-citations
- like-names, initial/full names, different fields
- field variation of citation amounts/purposes
- fluctuation of influence/use
- typos
Bibliometric Laws
- Seek to describe the working of science by
mathematical means. Generally that a few entities
account for the many citations.
• Bradford’s Law of Scattering
• Lotka’s Law
• Zipf’s Law
Bradford’s Law of Scattering
- How literature in a subject in distributed in
journals.
• “If scientific journals are arranged in order of decreasing
productivity of articles on a given subject, they may be
divided into a nucleus of periodicals more particularly
devoted to the subject and several other groups of zones
containing the same number of articles as the nucleus.”
- 9 journals had 429 articles, the next 59 had 499, the
last 258 had 404.
- Bradford discovered this regularity of calculating
the number of titles in each of the three groups: 9
titles, 9x5 titles, 9x5x5 titles.
- Can be influenced by sample size, area of
specialization and journal policies.
Brookes on Bradford’s Formula
- “The index terms assigned to documents also
follow a Bradford distribution because those terms
most frequently assigned become less and less
specific and therefore increasingly ineffective in
retrieval.”
Bradford’s Formula Itself
- Bradford’s Formula makes it possible to estimate
how many of the most productive sources would
yield any specified fraction p of the total number of
items. The formula is:
•
R(n) = N log n/s (1 <_ n <_ N)
- where R(n) = cumulative total of items contributed by
the sources of rank 1 to n.
- N = total number of contributing sources
- s = a constant characteristic of the literature
- then
- R(N) = N log N/s
- is the total number of items contributed by N sources.
More Bradford’s Law
- Citations originally counted year by year can be
expressed as the geometric sequence:
- R, Ra, Ra2, Ra3, Ra4, ..., Rat-1
• where R = presumed number of citations during
the first year, some of which do not immediately
emerge in publication. But as a<1, the sum of
the sequence converges to the finite limit R/(1a).
Lotka’s Law
- An inverse square law that for every 100 authors
contributing on article, 25 will contribute 2, 11 will
contribute 3 and 6 will contribute 4.
- formula is- 1:n2.
- Voos found 1:n3.5 for Info Science (1974).
- What are other similar analysis tasks you could
use Lotka’s law for?
- Are users, browsers, bloggers like authors?
Zipf’s Law
• The distribution which applied to word
frequency in a text states that the nth ranking
word will appear k/n times, where k is a
constant for that text.
- It is easier to choose and use familiar words, therefore
probabilities of occurrence of familiar words is higher.
rf=C rank, frequency,
- This can be applied by counting all of the words in a
document (minus some words in a stop list - common
words (the, therefore...)) with the most frequent
occurrences representing the subject matter of the
document. Could also use relative frequency (more
often than expected) instead of absolute frequency.
Wyllys on Zipf’s Law
- Surprisingly constrained relationship between rank
and frequency in natural language.
- Zipf said the fundamental reason for human
behavior : the striving to minimize effort.
- Mandelbrot - further refinement of Zipf’s law:
(r+m)Bf=c where r is the rank of a word, f is its
frequency, m, B and c are constants dependent on
the corpus. m has the greatest effect when r is
small.
Optimum utility of articles?
- the most compact library is not the least costly
because you get rid of articles more quickly
therefore you buy more.
- fewer articles are acquired and kept longer but
more shelf space and maintenance is needed.
- the challenge is to keep the most frequently
accessed available.
Goffman’s Theory
• His General Theory of Information Systems
• Ideas are “endemic” with minor outbreaks
occurring from time to time. Cycles of use.
Like memes and paradigm shifts (Kuhn).
Based on epidemiology and Shannon’s
communications theory.
Online Article Life
• Burton proposed a measure for the decay in
citations to older literature, a “half-life”
- How is this different on the net?
• a shorter life?
• older sites referred less, more?
• commercial sites vs. private sites.
• advertised vs word of mouth?
• linked from popular pages?
Price’s Law
- “half of the scientific papers are contributed by the
square root of the total number of scientific
authors”
• Leads to:
- bibliographic coupling - the number of reference
two papers have in common, as a measure of their
similarity, a clustering based on this measure
yields meaningful groupings of papers for
information retrieval.
Cumulative advantage model
- Price noticed this advantage
- Success breeds success. also implies that an
obsolescence factor is at work. You get mentioned
a lot, you get mentioned in more and more cited
papers.
- Polya describes this as “contagion”
Bibliometrics on the Web
• We can use these techniques, rules and
formulas to analyze Web usage.
- Like a bibliometric index for historical analysis.
• Key question: are citations like page
browsing/using?
• Using Web Servers Effectively
• Server Logs give us much data to mine
• Studies on the Web
Understanding the Web
• User-based data collection
• Surveys
- GVU, Nielsen and GNN
• Qualitative questions
- phone
- web forms
• Self-selected sample problems
- random selection
- oversample
Understanding the Web
• Web Servers
- Serve:
• text
• graphics
• CGI
• XMLHTTPRequest (REST, AJAX)
• Web services (SOAP)
- other MIME types
• Server Logs represent this activity
- A lot of empirical, quantitative data on use
Problems with Web Servers
• Not as Foolproof as Print
• No State Information
- Interaction with Web pages or Web apps is difficult
to log & analyze
• Server Hits not Representative
- Counters inaccurate
- Different, non HTTP requests & effects
• Floods/Bandwidth can Stop “intended” usage
• Robots, Spam, (D)DoS, Caching, etc.
Web Server Records
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Server-based
Proxy-based
Client-based
Network-based
Clever Web Content Setup
• unique file and directory names
• clear, consistent structure
• FTP server for file transfer
- frees up logs and server!
• Judicious use of links
• Wise MIME types
- some hard/impossible to log
Clever Web Server Setup
• Redirect CGI to find referrer
• Use a database
- store web content
- record usage data
• create state information with programming
- NSAPI
- ActiveX
• Have contact information
• Have purpose statements
• Bibliometric Servlets?
Managing Log Files
•
•
•
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Backup
Store Results or Logs?
Beginning New Logs
Posting Results
Log File Format
• see Appendix
• key advantage:
- computer storage cost decreases while paper cost
rises
• every server generates slightly different logs
Extended Log File Formats
• WWW Consortium Standards
• Will automatically record much of what is
programmatically done now.
-
faster
more accurate
standard baselines for comparison
graphics standards
Log Analysis Tools
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Analog
WWWStat
GetStats
Perl Scripts
Commercial Tools
Log Analysis Cumulative Sample
Program started at Tue-03-Dec-2006 01:20 local time.
Analysed requests from Thu-28-Jul-2003 20:31 to Mon-02-Dec-2003
23:59 (858.1 days).
Total successful requests: 4 282 156 (88 952)
Average successful requests per day: 4 990 (12 707)
Total successful requests for pages: 1 058 526 (17 492)
Total failed requests: 88 633 (1 649)
Total redirected requests: 14 457 (197)
Number of distinct files requested: 9 638 (2 268)
Number of distinct hosts served: 311 878 (11 284)
Number of new hosts served in last 7 days: 7 020
Corrupt logfile lines: 262
Unwanted logfile entries: 976
Total data transferred: 23 953 Mbytes (510 619 kbytes)
Average data transferred per day: 28 582 kbytes (72 946 kbytes)
Downie and Web Usage
• User-based analyses
- who
- where
- what
• File-based analyses
- amount
• Request analyses
- conform (loosely) to Zipf’s Law
• Byte-based analyses
Neat Bibliometric Web Tricks
• use a search engine to find references
- “link:www.ischool.utexas/~donturn”
• key to using unique names
- use many engines
• update times different
• blocking mechanisms are different
• use Google News (and the like)
- look for references
- look for IP addresses of users
Neat Tricks, cont.
• Walking up the Links
- follow URL’s upward
• Reverse Sort
- look for relations
• Use your own robot to index
- test
Projects
- capture current and previous user information
seeking behavior and modify interface and content
to meet needs
- Dynamic Web Publishing System
• anticipate information seeking behavior
• based on recorded preferences and presupplied rules, generate and guide users
through a document space.
Summary
• Bibliometrics, now Informetrics
• Bradford’s - distribution of documents in a
specific discipline
• Lotka’s - number of authors of varying
productivity
• Zipf’s - word frequency rankings
• The Web
- out of control in growth = opportunities
- wise setup can help
- use good analysis tools
Projects & Papers
• Everyone have topic or project?
• Let’s talk more (via email too) about ideas and
projects