Science Codex, August 4, 2014
The team has tested its algorithm on sets of several hundred articles comparing the automated quality assessment by the computer with assessment by a human user. Their algorithm out-performs a human user by up to 23 percent in correctly classifying the quality rank of a given article in the set, the team reports. The use of a computerized system to provide a quality standard for Wikipedia entries would avoid the subjective need to have people classify each entry. It could thus improve the standard as well as provide a basis for an improved reputation for the online encyclopedia.
http://www.sciencecodex.com/wiki_ranking-139094
Wikipedia the
free, online collaborative encyclopedia is an important source of information.
However, while the team of volunteer editors endeavors to maintain high
standards, there are occasionally problems with the veracity of content,
deliberate vandalism and incomplete entries. Writing in the International
Journal of Information Quality, computer scientists in China
Jingyu Han and
Kejia Chen of Nanjing University of Posts and Telecommunications, explain that
the quality of data on Wikipedia has for many years been the focus of user
attention. Its detractors suggest that it can never be a valid information
source in the way that a proprietary encyclopedia might be because the
contributors and editors are not under the direct control of a single publisher
with a vested interest in quality control. Its supporters suggest that the
social nature of contributions and edits and the online tracking of changes is
one of Wikipedia's greatest strengths rather than a weakness.
Nevertheless, it
would quiet the detractors if there were a way to quantify the quality of
Wikipedia entries in an objective and automated manner. Now, Han and Chen have
turned to Bayesian statistics to help them create just such a system. The
notion of finding evidence based on an analysis of probabilities was first
described by 18th Century mathematician and theologian Thomas Bayes. Bayesian
probabilities were then utilized by Pierre-Simon Laplace to pioneer a new
statistical method. Today, Bayesian analysis is commonly used to assess the
content of emails and to determine the probability that the content is spam, junk
mail, and so filter it from the user's inbox if the probability is high.
Han and Chen have
now used dynamic Bayesian network (DBN) to analyze in a similar manner the
content of Wikipedia entries. They apply multivariate Gaussian distribution
modeling to the DBN analysis, which gives them a distribution of the quality of
each article so that entries might be ranked. Very low-ranking entries might be
flagged for editorial attention to raise the quality. By contrast, high-ranking
entries could be marked in some way as the definitive entry so that such an
entry is not subsequently overwritten with lower quality information.
The team has tested its algorithm on sets of several hundred articles comparing the automated quality assessment by the computer with assessment by a human user. Their algorithm out-performs a human user by up to 23 percent in correctly classifying the quality rank of a given article in the set, the team reports. The use of a computerized system to provide a quality standard for Wikipedia entries would avoid the subjective need to have people classify each entry. It could thus improve the standard as well as provide a basis for an improved reputation for the online encyclopedia.
Source: Inderscience
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http://www.sciencecodex.com/wiki_ranking-139094
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