Measuring Wikipedia Article Quality By Revision Count

Measuring Wikipedia Article Quality By Revision Count


1 Introduction. 1

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1.1 About Wikipedia. 1

1.2 Featured Articles. 3

1.3 Random articles. 5

Analysis by word count. 6

Distribution of word count for featured and random articles. 7

Conclusion. 9

Qualitative work. 10

Quantitative Work. 10

Stabilized article. 12

Controversial Article. 14

Categorizing controversial and stabilized Articles. 15

Evaluation of revision history (user trust and fragmented sentence trust). 16

Bibliography. 18


1 Introduction

1.1 About Wikipedia

Wikipedia is the most popular free web site encyclopedia used by many browsers to create and revise shared documents for reference in research and study work.  According to Alexa web traffic ranking (2011), Wikipedia is the most used learners’ website with over 3.5 million articles in 200 languages that is approximately one million articles written in the English version. Since the articles are written by users with or without expertise, the Wikipedia is the most reliable and accurate source wide information and presentation (McGuinness & Bhaowal 2006).

Recently, due to the expansion, reliability, relevancy, and accuracy in its content and due to high ranking its articles, Wikipedia web engines has provided a wide range of information. The approach taken by the web is different from other encyclopedia paving way for consideration of views from people of diverse backgrounds, knowledge, skills, expertise, and experiences. The web is open for critical thinking and analysis online research for one to be able to make responsible conclusions and recommendations (Stvilia  & Gasser, 2005).

Comparative analysis on the quality and reliability of the Wikipedia articles, research has shown it to be of high quality and well researched source of information supported by most recent studies. This is supported from the work published recently by Nature Magazine (2011) that ranked Wikipedia comparable with Encyclopedia Brotannica the most ancient source of reference that have been kept up to date with additional information over time. The study conducted showed numerous common errors in both but the kind of information provided from them is more dependable (McGuinness  & Bhaowal 2006). Rigorous mechanisms have been employed for Wikipedia to maintain high quality information published. Before the articles are published in the Wikipedia, any articles is supposed to pass through peer-review scrutiny that recommends its publication, correction or being rejected. An article passes through a number of editorial communities and the editors measure the articles quality, accuracy and reliability before approved for use on the Wikipedia (McGuinness & Bhaowal 2006).

Due to its simple nature to access and understand, Wikipedia has high web visibility in its role of information collection and dissemination. It is based to a varying quality in article presentations. However, Wikipedia has faced a lot of challenges; it has also gained trustworthy due to the revision counts of articles and reducing erroneous information that can mislead researchers (McGuinness  & Bhaowal 2006). The popularity of Wikipedia is due to the fact that the articles are written by volunteer users, instead of paid experts. Therefore the kind of information given remains trustworthy and eliminates bias to the paid experts whom may mislead for the shake of getting paid. The is open to anyone for access of articles, modification of the already existing one or creates new articles to the web. It is site that gives one free access to the sum of all human knowledge (Stvilia  & Gasser, 2005).

Lack of essential information, accuracy and poor writing of articles poses a great challenge to the quality and application of the Wikipedia articles. As Nicholas Carr (2009) put it that, “this is garbage, an incoherent hodge-podge of dubious factoids that adds up to something far less than the sum of its parts”. The good thing, more accurate information may also be found in the same Wikipedia. A lot of criticism has been put forward to challenge the accuracy and reliability.

Vandalism has caused lot damage to the articles published in Wikipedia. In order to deal with the challenge, mechanism has been devices through the history link at the pages. It organizes their work in two ways. The first way is qualitative level upon which the work uses the total number of edits and unique editor count to measure the article (Cross, 2010). In this level the color text, font, and spacing is determined for the users could immediately understand the quality. The second is quantitative level where machine learning is used by researchers for measuring the quality of the articles and produce algorithmic methods of measurement (Cross, 2010).

Wikipedia is one of the globally internet encyclopedia accessible to anyone to participate in the online publication and preservation of knowledge. Diversity and experience brings together different kinds of information for users for exposure to a wide diversity of knowledge, opinions, ideas, views, and even reservations. The openness of the web, allows the anonymous and unregistered Wikipedia users to play a significant part to the new and existig Wikipedia articles. Wikipedia is philosophy is that as the content becomes more reliable and accurate over time when the community works together on the content. As a result of this, the articles created on Wikipedia are never “finished” as the addition, correction and collaboration are dynamic (McGuinness  & Bhaowal 2006).

Due to lack of formal peer review, the Wikipedia is subjected to vandalism and access to misleading information to make wrongful judgment in research. Self interested parties on the web can the take the openness advantage also to misinform others through this web (Stvilia  & Gasser, 2005).

1.2 Featured Articles

Many visitors of the Wikipedia become very difficult to trust the content found due to high variance in quality and reliability. For exceptional content quality variance, Wikipedia has taken special attention on articles with exceptional quality through grouping them “featured” articles. They are the most trusted, reliable and accurate articles for web users. As explains, “featured articles contain the best quality that Wikipedia has to offer”.  These are well researched, prepared and presented articles determined by Wikipedia editors and their contributions emanates from collaborative works organized in the Wikipedia internet services (Zeng & McGuinness 2006). The articles are reviewed through diverse method and criteria for determination of accuracy, reliability, neutrality, trustworthiness, completeness, and style used in presentation of the article.

The featured articled are well written, comprehensive and must explain major details and facts concerning the topic. It must be neutral and stable to the fact that it is fair and without any bias without changes as time goes by contents on the featured articles undergo a rigorous and thorough process of review to ensure that high standards are met. The peer-review process involves a group of competitive editors designated for careful scrutiny of every article published on the Wikipedia (McGuinness  & Bhaowal 2006).

The most unfortunate thing is that among the thousand articles published one article may be marked as a featured article. This leaves a challenge to the users to decide the quality, reliability, and accuracy of the article. Whilst many articles are indicated with similar metadata to denote low quality articles, majority of the articles found on the Wikipedia does not have such marking for the users to be sensitive and decide how to trust such articles. It is very uncertain for motivated researchers to find mechanisms for determining the quality of an article (Speigelhalter & Thoma 2005).

The findings from Blumenstock (2008) suggested that word count alone can differentiate featured Wikipedia articles from random Wikipedia articles. From another thinking perspective this conclusion may be intuitive; featured articles should belong for an article to be featured.

The word count is the qualification for an article to be a featured article. It is tested that revision count outdoes complex techniques in the classification of articles. Lengthy articles are thought to have gone through by several people and therefore have more knowledge and detailed information. Collaborative work of the Wikipedia forces articles to be long articles and of high quality (McGuinness  & Bhaowal 2006).

Although using featured articles as a proxy for quality, a higher standard of quality measurement is still required. Organizing human reviewers and editors are very costly and subjective and to come up with quality ratings such as Wikiproject Biography or Assessment offer great opportunity for future research works or web users. In some occasions, a long article may not be featured and a short article be featured or a long article featured with low quality than a short article not featured with high quality in content. Therefore not all long Wikipedia articles are high quality and featured.  Through collaborative works the quality of an article keeps growing and improves over time. A short article may be of high quality however not with the Wikipedia context (Speigelhalter & Thoma 2005).

On the Wikipedia Encyclopedia, featured articles are denoted by a small bronze star icon () on the top right corner unless the appropriate preference is set by the user (Wikipedia, 2012). This is meant to show the user has trustworthy on the article in relation to its accuracy and reliability.

1.3 Random articles

These are Wikipedia articles considered to have very little information or content in that they are short articles. Any Wikipedia browser can view a random article. This means that a random article can come from high quality articles, however, mostly random articles are chosen from other articles of low quality. Whilst a word count at the featured Wikipedia article is around 2700 words, average random Wikipedia article word count is 200 (McGuinness  & Bhaowal 2006).

Random articles are very short, and this implies that very little revision count work and less collaboration that has been done on them. The work then cannot be relied or accurate for its application in research and study. The revision count was found to be the most correct method to measure the accuracy and reliability of any content from non-featured Wikipedia articles. For instance, all articles with more than 2,000 words are classified “featured “and those with less than 2,000 words as “random” to achieve expected level of accuracy. Article with less than 2,000 revision count were considered not reach the cutoff threshold accuracy requirement for any article to be featured (Speigelhalter & Thoma 2005).

Computing as a classifier method is a very costly as it takes much time in fetching the data compute.  The article considered to be a collection of one author in one revision is regarded to be of very low quality to be trusted. The revision count of very many authors signifies a collective work of many authors with a lot of information put together and hence the level of accuracy and reliability is high. It refers to the action of editing through revision, addition, and correction of errors in the work done. Random article are considered to be the work of a single author and thus should not be trusted due to low quality in reliability and accuracy. Random articles are also considered to not neutral and hence are more biased to the direction of the author, level of experience, techniques, and exposure in the society (Hasan & Andr´e Gon, 2009).

Analysis by word count

Word count is a much simpler method of measuring the quality of Wikipedia articles as compared to the use of complex quantitative methods. In this method, the length of the article is measured by calculating the number of words in it. As far as there are limitations to this metric, there are substantial reasons to prove that this method will be compared to quality. Due to Absence of complication by this cadent, they presents the following advantages.

  • Measurement of the article length becomes easy
  • Length of the article performs significantly much better than the other methods
  • Most of the approaches mentioned earlier requires complex information for calculation e.g., history and revision of article (Speigelhalter & Thoma 2005).
  • Other methods mostly operate in an old fashion which constitutes hidden results and parameters that are to be decoded by average Wikipedia visitors.

Distribution of word count for featured and random articles

An experiment was conducted to test the article length performance to separate low and high quality articles a procedure formulated by Zeng and Stvilia et al. Instead of comparing scalar measure of article quality against metric, it was assumed that random articles are of lower quality than the featured articles. The goal was to maximize precision and recall of non-featured and featured articles (Speigelhalter & Thoma 2005). To make the conclusion 5,654,236 articles from the 7/28/2007 archives of English Wikipedia were extracted as shown in Table 1 below.


Class n TP rate FP rate PRECISION RECALL F-measure
FEATURED 1554 0.936 0.023 0.871 0.936 0.902
RANDOM 9513 0.977 0.064 0.989 0.977 0.983


TABLE I: Word count performance in classification of random Vs. featured articles.

Specialized files (images and templates) and those articles that contained less than fifty words were removed after stripping the Wikipedia- related markup. This resulted to cleaning of the data set which contained 1,554 featured articles. Further additional 9,513 cleaned articles which served as non-featured corpus were randomly selected. The total corpus added up to 11,067 articles. To further prove in another experiment 2/3(7378) articles for training were used and 1/3(3689) articles for testing, with a similar ratio of random: featured articles on each set.

The results showed that by classification of articles with more than 2000 words representing featured and those with less than 2000 representing random,  96.31% accuracy was achieved in binary in binary classification task. The results were achieved by minimizing the rate of error on the training set. The accuracy reported results from testing on the held out test set. More sophisticated classification techniques could lead to produce of the modest improvements. As an example, a multi-layer perception with an overall accuracy of 97.15% was archived with an F- measure of .983 for random articles and .902 for measured articles. The k-nearest neighbor classifier replicated similar results of 96.94% accuracy and a log it model showed 96.74% accuracy. A random forest classifier showed 95.80% accuracy (Hasan & Andr´e Gon, 2009).

All these techniques shows that word count is a more reliable method of quality measurement over the more complex methods in Zeng et al and Stvilia et al  which showed 86% and 84% accuracy respectively.

Word count matrices have proven to be very accurate which raised the curiosity of whether the other simple increased classification accuracy. Features like readability metrics, part of speech tags and n-gram bag of words have proven to be moderately successful in other contexts. In Wikipedia quality context, however it was noted that word count was unbeatable. N-gram bag of words classification indicated 81% accuracy for an example. Ie, n-1, 2,3 on both Bayesian and sym classifiers. A slightly higher accuracy of 96.46% was achieved after using 1A which is slightly higher with a thresh hold of 1,830 words (Hasan & Andr´e Gon, 2009).

Even after conducting another experiment on “kitchen sink” with 30 fixtures it was noted that the classifier achieved more than 97.99% accuracy which is an improvement given that considerable effort is required to build the classifies and produce this metrics (Stvilia & Twidale 2005).


It has been proven that the article length is a good way of determining whether the article will be included in Wikipedia. Word count has proven to be a simple method of metric that is by far more accurate than the other complex methods as proposed in related works done previously. It also performs a well independent classification parameter and a logarithm. We cannot exaggerate the efficiency of this metric by assuming that it features accurate measurement for quality because it is indicated that article length can also be used to determine the article quality. We can conclude that featured articles are long and long articles are featured (Adler & de Alfaro. 2007).

Qualitative work

Apart from the editorial guidelines in the, substantial qualitative work has developed with the aim of helping people to understand quality Wikipedia particularly and the encyclopedia in general. For example, according to Crawford (2001), he presented a thorough framework of assessing the quality of the encyclopedia. Further lih (2003) proposed metrics for online context. He also analyzed the correlation between unique authors of Wikipedia articles and the numbers of revision as well as the quality of these numbers. He proposed using unique editors and the total number of edits to measure the quality of article and later in 2006 he suggested the use of color according to age in order to give visitors some indication of quality (John & Langley 1995).


Quantitative Work

A more complex system for measuring the quality of an article has been designed and developed by researchers. This system basically relies on machine techniques of learning with the aim of producing alogarithm methods of measurement. Three steps are involved in the standard methodology (John & Langley 1995). They include;

  • Feature extraction

It involves the presentation of each article as a combination of various quantifiable metrics. This metrics are called features and might include straight forward information like word count, syllable count, number of references, sentence count, number of links and linguistic information like number of noun phrases, ratio of verbs and adverbs: revision through history like number of unique editors and edit count.etc. (Hasan & Andr´e Gon, 2009).

  • Evaluation

Te quality predicted is measured against the objective standard of quality. Few studies like the most resent work of de Alfaro and Adler (2007) have included the use of human experts in judging the quality of the predictions. Use of featured articles as an approximation of quality is the most common approach. The algorithm in step 3 will correctly put into place each article as a not featured or featured article, the accuracy is hece measured by dividing the number of classified articles with the number of correct classification. The main advantage of this method is that it is objective oriented and automatic. When an effective measure of quality is identified it can be applied min any article on Wikipedia. Following this methodology, Stvilia et al. tried to come with a system that would determine the quality of an article based on quality standards described by Crawford (2001). Seven factors were named by Crawford which were important to measuring quality; uniqueness, scope, format, authority, currency, accessibility, and accuracy. These factors were then transformed to quantifiable composites by Stivilia et al as shown in the following metrics.

  1. Consistency=0.6*Admin. Edit Share+0.5*Age
  2. Authority/Reputation=0.2*Unique Editors+0.2*Total Edits+0.1*Conectivity+0.3*Reverts+0.2*External links+0.1*Registered User Edits+0.2*Anonymous User Edits.

After computing these factors for each article he then cluster analysis to determine whether each article was featured or not. 86% overall accuracy was achieved. Similarly Zeng et al (2006) formulated a method of trying to measure the “trust” of article based on their edit history. In this case the relevant calculations the number of deletions, number of revisions and the number of blocked authors who edited each article. In regard to these features, he used a dynamic Bayesian network to create evolution of each article. He observed that he could classify featured articles at 84% accuracy.


  1. Classification/Quality prediction.

Use of Algorithms predicts the quality of an article on the basis of its features. For example, if believing that ages of articles is the most important feature, it is okay to predict that old articles are better in quality than new articles   (John & Langley 1995).

Contrary to these complex methods elaborated above, Blumenstock, (2008) formulated how features with more than 97% accuracy can be identified.  Its potential applications and results are discussed below.

Stabilized article

A stabilized article is the one that has more or less to do with the total knowledge of the subject matter of the topic. This article is considered complete content wise. Topics in stabilized articles mostly refer to notions, events, people, etc. with no chance of changing over time. Changes that happen in this type of articles are mostly related to revision or maintenance such as those made by automatic bots for updating the articles categories and the reverts of random vandalized attack. It is expected that significant accuracy is paramount in stabilized articles content since they are supposed to be complete content wise and to the total topic knowledge.


Wikipedia’s featured articles can serve as benchmarks of quality to model the stabilized articles quality. Some of the better written complete articles are featured in Wikipedia on a rotating basis. A policy of Wikipedia mandates that all the articles featured must be stable. Their content should not be subjected to ongoing edition war or do not change significantly from day today. For this reason stabilized articles aspire to be like the featured article essentially (Hasan & Andr´e Gon, 2009).


Our proposed model of quality uses articles features collection based on the notion that the articles features except for length can be handled like vital building blocks for an article. These are features like citations, images, paragraphs and references which are all essentials of a quality article. Nevertheless, excessive use of these building blocks can over or underdevelops an article.


There are no efforts in determining best features of stabilized articles because stabilized models are intended to be simple models as part of a more complex article classification schemes. This leads us to choose features which appear more reasonable and simple to extract for stabilized articles.

Featured articles act as quality benchmarks. The model expects that if a stabilized Wikipedia article appears to have exactly the same characteristic proportion to the featured article it is possible that it will affect the article length and quality very strongly. All the same, if the articles characteristics differ with those of the featured article, it diminishes the influence on the article length. Samples of featured articles wiki-pedia are required in this model. The sample is elaborated as a collection of different components of a mixture model. Six mixture models exist within this mixture model and they are acquired from the featured articles sample set. The components are mostly Gaussian probability density functions for computation of length, internal link density, image count density, citation density, internal link density, and section count density.  Following is how a single mixture component is computed (Hasan & Andr´e Gon, 2009).

Where the components are mean value and is the components standard deviation. In this case the length in bites component shows a Gaussian probability density function of the length in bytes of a sample featured articles. Normalized sum of mixture component represents the quality of the article.


Controversial Article

Controversial articles are articles whose content is due to raise different opinions. The policy on Wikipedia editor requires neutral view narratives. Never the less editors at Wikipedia are human are prone to biasness that influence how they edit intentionally or unintentionally. Other editors on detection of such biasness they may disagree with them making the article a subject of controversy.  Some of the articles contain inherited controversy due to their subject content. This may include articles on religion or ancient cultures that are passed down generations. Some article may go through the phase of controversy due to the attention they grab at specific times like eye-raising current events (McGuinness & Bhaowal 2006).

Most of the times, controversial articles are a weak target of sabotage and acts as a combat zone for reverts events. Historically controversial articles could be identified by how large the number of vandals and revert wars as well as anonymous contributions they attracted. Today we determine the quality of controversial articles by taking into consideration their revision history. The model used to determine the quality of controversial articles is very similar to that used to determine the quality of stabilized articles although it contains different article features (Hasan & Andr´e Gon, 2009).


The following table is a representation of a controversial model.

Feature Name Description
 Avg. Number of Reverts Average number of reverts in the article’s

revision history

Revisions Per Registered User Average revisions per registered authors
Revisions Per Anonymous User Average revisions per anonymous authors
Percentage of Anonymous Users Percentage of anonymous authors


Categorizing controversial and stabilized Articles

Before applying a quality model for either controversial or stabilized articles to a specific Wikipedia article, it is important to determine first if the article is controversial or stabilized or it belongs to another different category. This is achieved by using supervised learning techniques of classification. A classifier is developed and trained for specific article category. Finding the category of a Wikipedia article involves a two-step process. First features of an article are extracted and ran against a battery of classifiers (McGuinness & Bhaowal 2006).

When the target article is positively classified in the classifier, a quality model that corresponds to that classifier is applied to the article. In case a a target article is classified as positive by more than one classifier the average of outputs of each applied quality model is considered as the final score of the targeted article. Lastly, if the target article is not show positive classification by any of the classifiers in the series, the stabilized model that article is applied as the final score. In this paper, we do not consider articles that are not positively classified by either classifier. Note that each classifier was qualified from a 96 Wikipedia articles dataset which was manually chosen to include a mixture of article type described earlier. The class labels for these data sets were assigned manually.  Among the algorithms that were used we chose the one that provided the best results as the algorithm for the final classifier. The (SMO) sequential minimal optimization learning algorithm used for training vector machines classifiers was chosen (Stvilia & Smith 2005).

 Evaluation of revision history (user trust and fragmented sentence trust)

The trust values of articles fragments’ are used to determine the trust value of the article. It is shown in the previous experiments that our models produced strong results on the worthiness of the articles. This also indicates good performance of the model at the fragment level. Never the less, since we are interested in a direct evaluation of the fragment trust, we will use a survey group of people where we will manually decide the article fragments trustworthiness (Stvilia & Gasser, 2005).

Fragments of the article in Wikipedia that user is viewing are displayed in different colors based on the trustworthiness when a visitor clicks the trust view tab. Fragments that have higher trustworthiness’ are displayed in a vibrant color than fragments with lower trustworthiness to help the users to have an insight on relative trust just by looking at the tab presentation of the article although issues like intuitive mapping from use of color to trustworthiness are still being investigated. The revision trust has a lot of benefits far beyond the trust in Wikipedia. Many applications can be built to fully utilize the trust information that is available. The users may have an option of viewing the most trustworthy versions of an article as well as the most recent one. In addition to this the model may provide an automated method of monitoring changes in trustworthiness, therefore, providing timely notifications of malicious content modification and vandalism (Stvilia  & Gasser, 2005).

Many measures have been taken to address the challenges of trust. E.g. privileges of many authors to create new articles were recently increased and in the resent past a new feature called article validation is being processed which will enable users to rate an article openly via a restricted form. In addition, Lih formulated a set of metrics to evaluate the Wikipedia articles quality among other factors like number of revisions.

Vi’egas et al presented a tool that visualized revision flow and at the same time which revealed various interesting patterns in Wikipedia. For example, it was noticed that half of the mass deletions were being reverted within two minutes.

Theories of trust computation have also been widely studied. For example, Kamvar et al introduced a reputation system that helped minimize the effect of malicious peers in p2p networks. Propagation of trust and was discussed by Guha in social networks like All these approaches are targeted on transitivity property of trust. That is if A trusts B and B does the same to C, then it would be automation that A trusts C to a certain level. Our model can be improved by development of author trust models that can model complicated author behaviors like letting a blocked author in some cases make trustworthy contributions.

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You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

Will anyone find out that I used your services?

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. Noone will ever know that you used our assignment help services. It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

How our Assignment  Help Service Works

1.      Place an order

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

2.      Pay for the order

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

3.      Track the progress

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

4.      Download the paper

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

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