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Web Mining for WikipediaThe Depth-Study Presentation

Liangjie Hong

Department of Computer Science & EngineeringLehigh University

December 17th, 2010

Liangjie Hong Web Mining for Wikipedia

Outline

1 Wikipedia and Research about Wikipedia

2 Wikipedia as Social Media

3 Wikipedia as an external resource

4 Discussions

5 References


Table of Contents




4 Discussions

5 References


The Success of Wikipedia

In many ways:

the 7th most visited website

17 million articles

365 million readers

257 active language editions


The Key Characteristics of Wikipedia

participants from all over the worlds, totally voluntarily

a vast reservoir of knowledge base

a rich platform for collaborations

a showcase of Web 2.0 technology


The Research about Wikipedia

over 170 publications on top venues (journals, conferences)

from Computer Science, Sociology to Economics

a wide range of aspects regarding Wikipedia

consensus and debates

from early stages of Wikipedia


The Research about Wikipedia

Researchers:

Fernanda B. Viegas, MIT Media lab → IBM

Ed H. Chi, Palo Alto Research Center

Daniel S. Weld, Professor of Computer Science andEngineering at the University of Washington

Aniket Kittur, Assistant Professor of Human-ComputerInteraction Institute at Carnegie Mellon University

Jure Leskovec, Assistant Professor of Computer Science atStanford University


Research Questions

What are motivations behind the contributors?

How collaborations have taken place between users?

What is the quality and trustworthiness of the content,compared to traditional expert-based encyclopedia?

How to utilize Wikipedia as a great external resource?


Table of Contents




4 Discussions

5 References


Wikipedia as Social Media

We will discuss:

User Interactions

Link structures & Topical Coverage

Quality Measurement


User Interactions

motivations

characteristics of collaboration

newcomersvandalism

policies, conflicts and coordinates

potential applicable


User Interactions: Motivations

The research concerning the motivations behind contributors ismostly conducted by surveys on a small group of users.

[Forte and Bruckman2005] 22 users from Wikipedia

[Kuznetsov2006] 102 volunteer students in New York University

[Nov2007] 140 users from Wikipedia

[Viegas2007] 29 image contributors from Wikipedia

[Schroera and Hertel2009] 106 users from Wikipedia German


User Interactions: Motivations

Some interesting findings:

ownership of articles [Forte and Bruckman2005]

“fun” and “ideology” are two strong reasons[Nov2007, Schroera and Hertel2009]

information should be free [Viegas2007]

articles can be improved by adding images [Viegas2007]

credits and recognition [Schroera and Hertel2009]


User Interactions: Motivations: Discussion

However, many of questions regarding Wikipedia are stillunanswered.

What are motivations behind users from different cultures?

Do motivations change over time?

Are there any active contributors who left Wikipedia and whatare their motivations?


User Interactions: Characteristics : Newcomers

It is important to understand how newcomers get involved inWikipedia.

What type of contribution made by newcomers?

What is the scope of their contribution?

How do they transit to senior users?


User Interactions: Characteristics : Newcomers

Some interesting findings :[Viegas et al.2004, Bryant et al.2005, Kittur et al.2007, Choi et al.2010]

first edits involved topics with expertise.

Wikipedia becomes more important as a whole

newcomers → vandalism

lacking awareness of the community

meta-activities increase over time


User Interactions: Characteristics: Users Work Over Time

From [Kittur et al.2007]

Figure: Direct work decrease over time

Figure: In-direct work increase over time


User Interactions: Characteristics: Users Work Over Time

From [Kittur et al.2007]

Figure: Changes of different types of work over time


User Interactions: Characteristics: Vandalism

From Wikipedia:

Vandalism is any addition, removal, or change of contentmade in a deliberate attempt to compromise the integrityof Wikipedia.



According to [Viegas et al.2004], there are five types of vandalism:

mass deletion

offensive copy

phony copyOn the “Chemistry”, the full text from the “Windows 98 readme” file was

inserted

phony redirection

idiosyncratic copyOn “Cat”, a reader posted a lengthy note on the Unix cat command

and indeed image vandalism! [Viegas2007]



Surprisingly, “vandalism” is usually detected very quickly andrevised back.

mass deletion (Median time: 2.8 minutes; Mean time 7.7days) [Viegas et al.2004]

user labeled vandalism (Median time: 11.3 min; Mean time2.1 days) [Kittur et al.2007]

the prob. of viewing a damage page is increasing[Priedhorsky et al.2007]



From [Viegas et al.2004]

Figure: Left: history flow for “Abortion” page, versions equally spaced;Right: history flow for “Abortion” page, spaced by date



Sometimes, it is hard to detect “vandalism” or “edit wars”.From [Viegas et al.2004]

Figure: “Chocolate” page spaced out by number of versions;

two users fought over whether a kind of chocolate sculpture called “coulage” really

existed and consequently, whether or not the paragraph about it should appear on the

page.



From [Kittur et al.2007, Priedhorsky et al.2007]

Figure: Small increase in proportion of edits marked as vandalism.

Figure: Probability of a typical view returning a damaged article.



Some policies eventually came out. Certain pages are in“protection mode” (e.g., the first page).


User Interactions: Policies and Coordination

Some indications of collaboration and coordination: From[Viegas et al.2007]



“Talk pages” are places to policy making and coordination.

Talk pages were characterized as places where conflict wasresolved. [Viegas et al.2004]

Talk pages seem quite effective to resolve conflicts and settleagreements. [Bryant et al.2005]

Talk pages serve as a place for planning and other types ofcoordination. [Viegas et al.2007]



A closer view of policies,[Butler et al.2008, Beschastnikh et al.2008, Kriplean et al.2008]:

policies expanded

the details of policies grow significantly.

Copyrights: 341 words → 3200 words.What Wikipedia is not: 541 words → 5031 words.Civility: 1741 words → 2131 words.Consensus: 132 words → 2054 words.Deletion: 405 words → 2349 words.

users frequently cite policies

“value systems” becomes vital



From [Beschastnikh et al.2008]:

Figure: Each striation is a single policy. The width represents the fractionof cumulative policy citations. The 50 bottom striations are ordered frommost cited policies in the last week of the dataset (bottom) to least cited(top). The top striation aggregates the remaining 142 policies.



From [Beschastnikh et al.2008]:

Figure: Each striation in this graph represents a single category. Thewidth of the striation represents the fraction of cumulative policycitations for policies in the category. Striations are ordered from bottomto top according to their rank in the last week of the dataset.



1443 recipients as observed by 1537 givers (382 administrators).These barnstar givers and receivers have contributed 9.7% and11.8% of all edits to Wikipedia articles, respectively.[Kriplean et al.2008]


User Interactions: Discussion

Many efforts are from the community of Computer-HumanInteraction, Sociology and Journalism. Some current issues:

Many results are based on surveys on a small scale.

Most of work only focus on Wikipedia English.

No work on interactions between articles and talk pages


Link Structures and Content Topical Coverage

Content of articles and the rich structures built between them aretwo heated research topics regarding Wikipedia.


Link Structures

Research questions:

Is the link structures of Wikipedia different from the Web?

What is the evolution of Wikipedia graph?

How Wikipedia graph helps us in various tasks?

Note, the rich “entities” in Wikipedia:

articles

users

categories

edit history

· · ·


Link Structures: Basic Observations

The graph of Wikipedia articles is similar to that of the Web.

“bow tie” structure exists [Capocci et al.2006, Buriol et al.2006]

indegree and outdegree distributions follow Power-lawdistribution [Capocci et al.2006, Buriol et al.2006, Kamps and Koolen2009]

preferential attachment [Capocci et al.2006]

the graph becomes more denser over time[Buriol et al.2006, Kamps and Koolen2009]

a unique growth process across different languages[Zlatic et al.2006]

Outlinks behave similar to inlinks. [Kamps and Koolen2009]


Link Structures: Edit Graph

Users participate into the revisions of articles and therefore, therelationships between articles, articles-users and users can beexploited in graph structures.

reveal same edit patterns among users [Jesus et al.2009]

revision actions are associated to users and build graph toidentify opposed groups, discover controversial topics[Vuong et al.2008, Brandes et al.2009]

However, these methods are only evaluated anecdotally or on asmall subset of Wikpedia.


Link Structures: Edit Graph

From [Vuong et al.2008]

Figure: Top Revision Count 20 Articles


Link Structures: Predicting Missing Links

Since Wikipedia is fully generated by volunteers, it is possible thatthe links between articles are missing or misplaced.

cluster similar pages → candidates [Adafre and de Rijke2005]

matrix factorization [West et al.2009]

However,:

no ground truth

human evaluation

anecdotal results


Topical Coverage

People wish to know how wide the topics Wikipedia has covered.Therefore, two kinds of coverage are investigated:

Comparable Coverage: the coverage compared to othersources

Independent Coverage: the coverage of topics in its owntaxonomy


Topical Coverage: Comparable Coverage

[Halavais and Lackaff2008] conducted two experiments:

3, 000 articles from Wikipedia

assign these articles into Library of Congress category

(at the broadest level)

manually by two labellers

compared to Bowkers Books in Print, a catalogue ofprinted books



From [Halavais and Lackaff2008]:

Figure: Wikipedia vs. Books in Print, percentage of total in eachcategory, ranked by percentage difference between the two collections.




Figure: Average number of edits of articles in each category



The second experiments done by [Halavais and Lackaff2008] is tocompare articles from Wikipedia with:

Encyclopedia of Linguistics

New Princeton Encyclopedia of Poetry and Poetics

Encyclopedia of Physics




Figure: Coverage comparison between printed encyclopedias andWikipedia


Topical Coverage: Independent Coverage

From [Kittur et al.2009]:

Figure: Distribution of topics in Wikipedia from January 2008 along withchange since July 2006


Topical Coverage: Independent Coverage

From [Kittur et al.2009]:

Figure: Distribution of conflict in Wikipedia. Sizes represent normalizedconflict,while the topic order (clockwise from People) reflects theabsolute amount.


Quality modeling and Trustworthiness

There is a huge debate that whether the content of Wikipedia is inhigh quality.



Research communities pay attention to this problem from 2004.



Research communities pay attention to this problem from 2004.No consensus.



Two types of “quality”.

The quality or trustworthiness compared to otherencyclopaedia

The quality of articles within Wikipedia


Quality modeling and Trustworthiness: “Absolute Quality”

All studies are surveys.

Yes. 42 articles, compared to Britannica. [Giles2005]

No. No real comparison, only concerns. [Denning et al.2005]

Yes. No real comparison, many side evidence. [Fallis2008]

Hard to judge. No transparency. [Santana and Wood2009]



The Nature study, [Giles2005]

factual errors or misleading statements in both sides.

writing style is an issue in Wikipedia

nearly 17% Nature authors among 1000 routinely referred toWikipedia

But,

there exist Wikipedia articles went wrong significantly

editors in Britannica refused to comment



An interesting fact: Wikipedia articles are frequently cited in newsmedia. [Lih2004]

72 news sources

three most cited articles are World War II, Islam andAstronomy

Daily Telegraph online, a third of citations


Quality modeling and Trustworthiness: “Relative Quality”

The task becomes to model and predict the “quality” of articles inWikipedia.

featured articles vs. normal articles (vandalism articles)

machine learning techniques

high accuracy



Features or indicators used in many methods:

revision history of articles[Stvilia et al.2005, Zeng et al.2006, Stein and Hess2007, Hu et al.2007a,

Hu et al.2007b, Wilkinson and Huberman2007, Wohner and Peters2009]

content of articles (e.g., the length)[Hu et al.2007a, Hu et al.2007b, Blumenstock2008]

reputation of authors and quality of articles[Hu et al.2007a, Hu et al.2007b, Stein and Hess2007]

links to external resources [Lopes and Carrico2008]



From [Blumenstock2008]

Figure: Word counts for featured/random article.



From [Blumenstock2008]

Figure: Performance of word count in classifying featured vs. randomarticles.


Quality modeling and Trustworthiness: Discuss

“Absolute Quality” needs more investigates to reach consensus.“Relative Quality” is “solved”.


Table of Contents




4 Discussions

5 References


Research Problems

A wide range of research communities are using Wikipedia as aresource to tackle the problems like:

Knowledge Extraction

Classification & Clustering

Entity Disambiguation

Information Retrieval & Web Search

and many others!


Structures of Wikipedia

The structures provided by Wikipedia:

redirect pages

categories

disambiguation pages

hyperlinks



Natural Language Processing (NLP):

building thesaurus dictionaries

measuring semantic “relatedness”

[Milne et al.2006, Strube and Ponzetto2006, Gabrilovich and Markovitch2007,

Nakayama et al.2007, Giuliano et al.2010]



Semantic Web:

Tim Berners-Lee’s compelling vision of Semantic Web ishindered by a chicken-and-egg problem, requiring richstructured data to motivate the development ofapplications.

[Wu and Weld2007, Weld et al.2008, Wu et al.2008, Wu and Weld2008]

a bootstrap tool to build knowledge base


Classification and Clustering

Document classification and clustering are two of the mostimportant applications of text mining.

enrich document representation

significant performance improved


Classification and Clustering

Ways to enrich document representation

category information[Schonhofen2006]

entities[Huang et al.2008, Wang et al.2009, Odon de Alencar et al.2010]

and usually better than WordNet or Open Directory Project


Entity Disambiguation

Entity disambiguation, especially Name Disambiguation, plays acentral role in many NLP applications (e.g., measuring relatedness,relation extraction).

Performance has been significantly improved by utilizing thestructures of Wikipedia.[Bunescu and Pasca2006, Mihalcea2007, Cucerzan2007, Han and Zhao2009,

Fogarolli2009]


Information Retrieval and Web Search

Wikipedia has shown being effective that can be used in a varietyof aspects in IR systems:

query expansion and pseudo relevance feedback[Li et al.2007, Xu et al.2009, Meij and de Rijke2010]

query segmentation[Tan and Peng2008]

cross-lingual IR[Potthast et al.2008]


Table of Contents




4 Discussions

5 References


Discussions

Current achievements:

“Relative Quality” assessment, semantic relatedness

general characteristics

many surveys conducted


Discussions

And issues:

hardly find that research projects have been conducted on thewhole dataset.

even the sampling methods are different

seldom compare to each other

nearly no study on the problem of applicable of the technology


Table of Contents




4 Discussions

5 References


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