Defining Quality in Web Search Results
Panagiotis Metaxas and Lilia Ivanova
Computer Science Department, Wellesley College, U.S.A.
Web search, adversarial information retrieval, web spam.
Web search results enjoy an increasingly greater importance in our daily lives. But what can be said about
their quality, especially when querying a controversial issue? The traditional information retrieval metrics of
precision and recall do not provide much insight in the case of the web. In this paper we examine new ways of
evaluating quality in search results: coverage and independence. We give examples on how these new metrics
can be calculated and what their values reveal regarding the two major search engines, Google and Yahoo.
The web has changed the way millions of people are
being informed and make decisions. Most of them use
search engines to access web information. Since peo-
ple use search engines daily to make all kinds of finan-
cial, medical, political or religious decisions, quality
of search results is of great importance. In the last
ten years the two major search engines, Google and
Yahoo, have gained the lion’s share in the search mar-
ket (Moran and Hunt, 2006).
But does higher market share implies higher
search quality? Performance of information retrieval
methods is traditionally measured in terms of preci-
sion (fraction of results that are relevant to a query)
and recall (fraction of relevant items included in the
results) (Manning et al., 2008). It is well known, how-
ever, that web searchers rarely look past the top-10
results (Silverstein et al., 1999). The web has enor-
mous size. More that 10 billion pages are indexed by
search engines and this represents a small portion of
the static web. At the same time, search results on im-
portant issues are being heavily spammed (Gyuongyi
and Garcia-Molina, 2005; Metaxas and Destefano,
2005). Therefore, high precision is easy to achieve
but does not convey useful information, while recall
cannot be computed accurately because of the enor-
mous size of the web.
The problem of measuring search result quality
becomes more interesting when searching for con-
troversial issues. A controversial issue is one that
has several possible relevant “answers”, depending on
one’s point of view. Are the results users receive char-
acterized by a reasonably comprehensive coverage?
In other words, are the various opinions equally rep-
resented in the search results?
While search engines are trying to provide unbi-
ased results, Search Engine Optimization (SEO) com-
panies and web spammers are actively trying to force
a search engine to list their own sites high on its search
results. They do so using a variety of techniques,
such as creating “link farms” (Gyuongyi and Garcia-
Molina, 2005; Moran and Hunt, 2006). How indepen-
dent are the top-10 results? For example, is it possible
for a successful group of spammer to claim, not only
the top spot in the top-10 search results, but a large
group of them?
In this paper we take on the problem of defining
coverage and independence of web search results. As
far as we know, even though several papers have tried
to define search quality (e.g., (Amento et al., 2000))
the metrics we introduce have never been addressed
in the past. In the process we study the structure and
density of the web neighborhood that supports each
of the web search results according to each search en-
gine, and we observe some interesting characteristics
of these neighborhoods and of the way the search en-
gines operate.
To address these questions we decided to do a se-
quence of web searches on highly contested issues us-
Metaxas P. and Ivanova L. (2008).
COVERAGE AND INDEPENDENCE - Defining Quality in Web Search Results.
In Proceedings of the Fourth International Conference on Web Information Systems and Technologies, pages 106-113
DOI: 10.5220/0001529201060113
Table 1: Top-10 results of the Google search engine when given the query ”HGH benefits” for August, 2007 and September,
2007. For each entry we have calculated the size of the backGraph as (|V|,|E|) revealed by the Google API and the change
between these two dates.
# Google results August, 2007 September, 2007 Change (%) Notes
1 (415,426) (346,353) -16.6 G1=Y8
2 (161,167) (133,137) -17.4 G2=Y1
3 growth hormone benefits.html (1245,1314) (981,1027) -21.2 G3=G4
4 (1245,1314) (981,1027) -21.2 (r)
5 (71,77) (71,77) 0.0 G5=G6
6 (71,77) (71,77) 0.0 (u)
7 growth hormone.htm (1,0) (1,0) 0.0 filter
growth hormone hgh benefits risks.html
(1749,2086) (2905,3386) +66.1
9 (1388, 1585) (463, 506) -66.7 G9=Y2
10 (502 ,506) (419 ,423) -16.6
Table 2: Top-10 results of the Yahoo search engine when given the query ”HGH benefits” for August, 2007 and September,
2007. For each entry we have calculated the size of the backGraph as (|V|,|E|) revealed by the Yahoo API and the change
between these two dates.
# Yahoo results August, 2007 September, 2007 Change (%) Notes
1 (13151,16690) (7829,9294) -40.5 Y1=G2
2 benefits.html (2933,3871) (3380,4634) +15.2 Y2=G9
3 (9587,11741) (6376,7727) -33.5 Y3=Y9
4 (2137,2402) (3125,3551) +46.2
5 (2933,3871) (3380, 4634) +15.2 (u)
6 (3063, 3444) (5575, 6877) +82.0
7 (3194, 3665) (491, 495) -84.6
8 (1041, 1204) (1990, 2295) +91.2 Y8=G1
9 (6376,7727) (6376,7727) 0.0 (u)
10 (5471, 6460) (7418, 9114) +35.6
ing the two most popular search engines, Google and
Yahoo. For each of the queries we selected, one can
expect that there are at least three possible answers:
a “pro”, a “con” and a “bal” (short, for “balanced”)
We argue that for a controversial issue, equal and
comprehensive coverage in the top-N results is to
have an equal number of pro, con and bal results. We
will simply refer to this quality as “coverage” and we
will define it below. For example, in the top-10 search
results that search engines are giving back by default,
equal and comprehensive coverage would be to have
3-4 results (or, on average, 3.3 results) from each cat-
Let’s assume we have k different categories for
a complete coverage (above we have k = 3) and we
have N results in the top-N slots. Let’s further assume
that category i received r
results. We define as cover-
age bias the following quantity B:
B =
| (1)
In other words, B is the distance of r
from the
expected number of results N/k. We note that bias B
is bounded by:
= 0 B N + (k 2)N/k = B
Specifically for top-10 search results, when we
have comprehensive and equal coverage, we have
minimum bias B
= 0. At the other end, when one
category takes all top-10 spots, the bias is maximized
at B
= 13.3.
The further bias B is from 0, the worst the cover-
age is. Therefore we can define as coverage C, the
lack of bias, that is,
C =
Coverage C, therefore, has a value between 0
(one-sided coverage) and 1 (equal and comprehensive
The second metric we introduce is search result
independence. To define independence we need first
to examine the various ways in which search results
can be dependent. We see three kinds of dependent
URL Dependency is the situation when multiple
entries in the top-N results are actually coming
COVERAGE AND INDEPENDENCE - Defining Quality in Web Search Results
Table 3: Top-10 results of the Google search engine when given the query ”Is ADHD a real disease” (August and September,
# Google results August, 2007 September, 2007 Change (%) Notes
1 (1928,2135) (1423,1614) -25.2 G1=Y6
2 (1223,1297) (912,991) -25.4 G2=Y9
3 (496,515) (873,925) +76.0 G3=G4
4 (496,515) (873,925) +76.0 (u)
5 (2545,2759) (1688,1790) -33.7 pro
6 (3280,3912) (3955,4791) +20.58 G6=Y3
7 (3280,3912) (3955,4791) +20.58 (u)
8 (1590,1708) (1557,1848) -2.1 G8=Y2
(1,0) (1,0) 0.0 (c)
10 Articles/Escolar-is-adhd-a-
(5062,5764) (incomplete) -99.9 (c)
Table 4: Top-10 results of the Yahoo search engine when given the query ”Is ADHD a real disease” (August and September,
# Yahoo results August, 2007 September, 2007 Change (%) Notes
1 (10263,13422) (12321,16184) -16.7
2 (7610,9556) (5208,6202) +46.12 Y2=G8
3 (7567,9093) (9791,12232) -22.7 Y3=G6
4 ADHD A Real Disease.html (13631,17032) (12590,15645) +8.3
5 (7011,8488) (6704,7993) +4.6 pro
6 (6425,8651) (4242,5148) +51.5 Y6=G1
7 (8424,10808) (8266,10184) +2.0 (l)
8 adhd a real disease.php (19291,25469) (21150,27145) -8.8 (c)
9 (5675,6644) (4920,5775) +15.37 Y9=G2
10 (2840,3440) (2359,2604) +20.4 (l)
from the same site URL, e.g., they correspond to
different pages of the very same site, as it is de-
fined by the domain URL. For example, in Ta-
ble 1, results numbered 5 and 6 have URL depen-
dency. In the Tables of this paper we mark one of
the two URL dependencies with a (u).
Redirection Dependency is the situation when two
different site URLs resolve onto the very same lo-
cation. Redirection is often used by web spam-
mers who try to increase the visibility of a target
site by creating many other sites that will point
to the target site (Gyuongyi and Garcia-Molina,
2005). For example, in Table 1, results numbered
3 and 4 have redirection dependency. In the Tables
we mark one of the two redirection dependencies
with an (r).
Content Dependency is the situation when the
contents of two or more pages included in the
search results are essentially the same. The con-
tents may be surrounded by images and menus
that are different and are stored on different web
sites. This is also a trick used by spammers who
try to increase visibility to a target site by cre-
ating entries in blogs or “news” sites that lack
their own content (Gyuongyi and Garcia-Molina,
2005). For example, in Table 4, results numbered
4 and 8 have content dependency. In the Tables we
mark one of the two content dependencies with a
Link Dependency is the situation when the sup-
porting link structure in the web graph is sub-
stantially similar in two or more sites. Link
dependency reveals “link farms’ of spammers
(Gyuongyi and Garcia-Molina, 2005). This type
of dependency has been studied extensively in the
literature, and it is considered a major tool that
the Search Engine Optimization industry is using
to acquire high PageRank (Brin and Page, 1998).
In this paper we will focus on the most basic sim-
ilarity structure, namely the Circular Link Depen-
dency between two or more sites. For example, in
Table 4, results numbered 3 and 10 have circular
link dependency, as do results numbered 6 and 7.
See Figure 1. In the Tables we mark one of the
two link dependencies with an (l).
Let’s consider the search results of some contro-
versial query. Let’s assume that out of the top-N re-
sults, u results are URL dependent, r results are redi-
WEBIST 2008 - International Conference on Web Information Systems and Technologies
Table 5: Top-10 results of the Google search engine when given the query ”Morality of abortion” (August and September,
# Google results August, 2007 September, 2007 Change (%) Notes
1 bsharvy/abortion.html (5942,6710) (5597,6350) -5.9 G1=Y1
2 (3922,4610) (3429,4138) -12.6 G2=G3
3 (3922,4610) (3429,4138) -12.6 (u)
4 (12045,14659) (10433,13102) -13.38 G4=Y8
5 (1667,2095) (2687,3213) +61.19
6 (7440,8657) (5187,6276) -30.28 G2 = Y6
(3006,3373) (2902,3211) -3.5
8 (6422,8119) (5810,7295) -9.5 G4 = Y8
9 b97.htm (2079,2353) (1331,1560) -36.0
10 documents/rfletcherFlagsubamd.pdf (incomplete) (incomplete) 0.0
Table 6: Top-10 results of the Yahoo search engine when given the query ”Morality of abortion” (August and September,
# Yahoo results August, 2007 September, 2007 Change (%) Notes
1 bsharvy/abortion.html (17224,21732) (15367,19843) -10.8 Y1=G1
2 (17491,22553) (16137,21632) -7.74
3 (13434,17522) (13752,17656) +2.37 Y3=Y9 (u)
4 and legality of abortion (25672,35167) (25672,35167) 0.0
5 (10282,14156) (12243,16284) +19.1
6 (6274, 8169) (6274, 8169) 0.0 Y6=G2
7 (7370,9919) (7370,9919) 0.0
8 (13877,18248) (13877,18248) 0.0 Y8-G4
9 (13752,17656) (13752,17656) 0.0 Y9 = Y3
10 (2195,2721) (incomplete) 0.0
Figure 1: Link dependencies between the top-10 sites of
Table 4.
rection dependent, c results are content dependent,
and l results are link dependent. We define as inde-
pendence of the search results the ratio of the non-
dependent sites over N, or
I =
N (u+ r+ c+ l)
Independence essentially measures the percentage
of independent results in a collection of top-N search
results. Note that in the formula above we penalize
URL dependencies for all but one (their “representa-
tive”) of the identical URLs. We do the same for all
other types of dependencies. Of course, we do not
penalize results having multiple dependencies more
than once.
Like coverage, independence is also a number be-
tween 0 (lack of independence)and 1 (all independent
results). As with coverage, the higher the indepen-
dence, the higher the quality of search results.
We should clarify that, even though it is a very im-
portant issue, we are not examining here the correct-
ness or factual accuracy of search results. It is known
that misinformation is a serious problem on the in-
ternet (Vedder, 2001; Berenson, 2000; Graham and
Metaxas, 2003). But evaluating correctness or accu-
racy require significant amounts of time by qualified
experts and there is no easy way to be done automat-
ically by computer. We are mainly interested in ex-
ploring quality measures that can be computed using
automatic or semi-automatic algorithms that will help
search engines increase the quality of their search re-
COVERAGE AND INDEPENDENCE - Defining Quality in Web Search Results
An examination of a daily newspaper will reveal
many controversial issues for which web users may
search, and our colleagues have recommended many
others. We chose to follow search results to three
queries coming from the commercial, medical and po-
litical arena:
Q1 (Commercial): Human growth hormone
(HGH) benefits
Q2 (Medical): Is ADHD a real disease ?
Q3 (Political): Morality of abortions
Previous research (Moran and Hunt, 2006) suggests
that Q1 is a highly contested query by small (mostly)
companies selling steroids on the internet. Q2 is
a highly contested query by activists claiming that
ADHD is the result of conspiracy by psychiatrists. Fi-
nally, Q3 is a perennially important issue in the USA
on which every politician must take a position and an
issue elections might be decided on.
The question we wanted to address is: Do we
get good coverage and independent responses when
searching on these issues using the two major search
engines, Google and Yahoo?
To evaluate our hypothesis we first conducted the
three searches using the Google and Yahoo search en-
gines in August, 2007. We analyzed the search results
and computed the coverage and independence of each
It has been shown (Ntoulas et al., 2004) that the
link structure of pages is evolving at a very fast pace,
faster than the page contents themselves. We wanted
to check this observation with regards to the web site
back links. Do they also change fast? To do that,
we followed the link structure of the supporting web
graph twice, in August and September 2007, with
our results reported here. We continue monitoring on
monthly basis.
We examined several questions regarding the
comparison of the results between the two search en-
gines. The results of our work appear in the subsec-
tions below. First, we explain how we computed the
supporting web graphs of each search result.
We define as the supporting web subgraph G of
depth d for some URL U according to search engine
S the graph that is computed using Ss back links for
d iterations.
To compute the supporting web subgraphs of each
search result, we created a java program,
that, given a particular URL U as input, it first col-
lects the set of links L(U) of sites pointing to U ac-
cording to search engine S. Since we cannot search
the whole web from scratch to determine these links,
we used the Search APIs provided by Yahoo (Yahoo,
2006) and Google (Google, 2003). This collection
corresponds to back link depth d = 1.
We continued computing the sets L
(U) =
L(L(U)) for depth d = 2 and L
(U) for depth d = 3
using each of the two APIs. We stop at depth d = 3
following (Metaxas and Destefano, 2005) that shows
is to be sufficient for calculation of the supporting
web graph. Going further would strengthen our re-
sults but would also require significantly larger com-
putational time.
More formally, the algorithm and the parameters
we used are as follows:
s[i] = each of top-10 results’ URL
d = Depth of back link search (d=3)
B = Number of backlinks to record (B=100)
SE = {YahooAPI, GoogleAPI}
S = {s[i]}
Using depth-first-search for depth d do:
Find the set U of sites linking to sites in S
using the SE for up to B backlinks/site
S = S + U
Graph recorded in S for each API
One may expect that L(L(L(U))) would create a
tree of sites pointing to U in no more than 3 links. It
turns out that the graph created is not a tree but a di-
rected graph G with a bi-connected core (called BCC
in (Metaxas and Destefano, 2005)). It has been shown
that this graph reveals the deliberate link support that
activists and spammers are using to promote a partic-
ular web page so that this page scores high on a search
engine’s query results. We then evaluated the size of
each graph |G| by calculating the number of nodes
(sites) |V| and edges (links) |E| as well as the size of
its BCC. In this paper, due to space considerations,
we do not present the BCC data.
3.1 Overall Results
Table 7 shows the coverage and independence results
of the three queries. We observe that in the com-
mercially important Q1 and the medically important
Q2, coverage from both search engines is very low.
Results from both search engines show also low in-
dependence. On the other hand, for the politically
important Q3, coverage and independence is high for
both search engines, with Yahoo scoring a little higher
in coverage than Google.
In the late 1990’ssearch engine algorithms used to
differ significantly. It has been argued that these days,
WEBIST 2008 - International Conference on Web Information Systems and Technologies
Table 7: Results for coverage and independence metrics. C(G) and C(Y) are the coverage scores in the Google and the Yahoo
results, respectively. I(G) and I(Y) are the independence scores for the Google and Yahoo results, respectively.
Query bal pro con C(G) bal pro con C(Y) I(G) I(Y)
Q1: HGH benefits 0 10 0 0.0 0 10 0 0.0 0.8 0.7
Q2: ADHD real disease 0 1 9 0.1 0 1 9 0.1 0.7 0.7
Q3: Morality of Abortion 5 3 1 0.7 3 2 4 0.8 0.9 0.9
most search engines use very similar search algo-
rithms. Our results provide evidence for this. About a
third or more of the results are shared for each query,
providing evidence that there is significant overlap in
the results reported by the search engines.
The size of the support web subgraph is one area
where the search results differ significantly between
the search engines. Yahoo’s supporting web graph
size is significantly larger than Google’s. Given the
popularity and reputation of Google, one might have
guessed the opposite.
We believe that the reported sizes of the support-
ing web graphs by Google are affected by some fil-
tering of the back links. This is strongly evidenced by
the fact that several supporting web graphs for Google
have trivial sizes (e.g., result 7 in Q1 and result 9 in
Q2), and this can be explained with the existence of
result filtering.
Next, we discuss results for each query in some detail.
3.1.1 Q1 Results
We observe the following:
The size of support web subgraphs reportedby Ya-
hoo is 7 times greater than that reported by Google
(total of 49886 nodes vs 6848 nodes). In the period
of the two months we monitored the sizes of the sup-
porting web subgraphs, we saw small-to-medium per-
centage variation in the size of the Google graphs and
wide range for the Yahoo graphs. Despite that, they
seem to almost agree on the top result (G2 = Y1) as
well as in other results (e.g., G1=Y8, G9=Y2).
All of the top-10 results for both Google and Ya-
hoo seem to be coming from companies that sell
steroids online (the “pro” case). There are no results
from medical authorities or research articles that re-
fer to the medical or legal problems from the use of
steroids (the “con” case). There are also no “bal-
anced” views represented in the top-10 results. Cov-
erage, therefore, is very low. We believe that these
results reveal the work of very successful commercial
SEOs who have dedicated lots of resources to gain
from the lucrative HGH industry.
3.1.2 Q2 Results
We observe the following:
As we mentioned, Q2 coverage results are largely
similar to Q1 results for both search engines. The
size of support web subgraphs reported by Yahoo is,
again, far greater than that reported by Google (total
of 88737 nodes vs 19901 nodes, or 4 times greater).
These results reveal low coverage, with only one
of the top-10 entries differing from the overall “con”
direction of the results. There are no “balanced” re-
sults included. Interestingly, for both search engines,
the “pro” result occupies position 5! The results for
Yahoo reveal low link independence as four of the
top-10 results are forming two circular link farms
(Figure 1).
We believe that when a controversial issue is be-
low the horizon of current news awareness, such as
the ADHD issue at the time of the search, activists
can be successful in getting the top spots in the rele-
vant queries. The situation seems to be a bit different
for issues that have enormous visibility, such as the
next query.
3.1.3 Q3 Results
We observe the following:
Yahoo’ssizes of support web subgraphs is roughly
equal to Google’s, (total of 125376 nodes vs 113463
nodes for the first nine results) in big contrast with
the results in Q1 and Q2. Interestingly, both engines
also agree on the top spot (G1=Y1), which represents
a “balanced” opinion of the query. They both in-
clude pro-choice” and “pro-life” results, while de-
voting the remaining top-10 entries on opinion gath-
erers (such as and
Over the two month period we observe much
smaller variation of the sizes of the supporting web
graph, especially for Yahoo. It is not the case that this
was due to lack of news for the issue of abortion at
the time of the searches. The abortion issue is always
at the top of the political agendas of the US candi-
dates for office, and has high visibility on the news.
We conjecture that for such highly sensitive issues,
the search engines “tune” their results so that they will
present wider coverage of opinions. We have seen this
happening in the past in a variety of well publicized
queries (such as ”miserable failure”).
COVERAGE AND INDEPENDENCE - Defining Quality in Web Search Results
Figure 2: The graph induced by the backlinks of Google’s 1
result and Yahoo’s 1
result for Q3 (G1=Y1). The graph has
been drawn to emphasize the two BCCs. The upper left group is composed by the majority of Google’s 5942 sites, the lower
right group is composed by most of Yahoo’s 17224 sites, while the group in the middle left side (appears as a horizontal line)
is mostly composed of 470 sites in the intersection of the two groups.
3.2 Graph Overlaps
Another question we are studying is how much over-
lap exists in the backGraphs produced by the two
search engines on the same result. Given that Yahoo
reports many more links than Google, one might ex-
pect that there is a major overlap between their back-
link graphs. We have found this not to be the case.
See Figure 2 for a graphical representation of a typi-
cal example. The two search engines seem to report a
largely different set of links in their results: Between
G1’s 5942 sites and Y1’s 17224 sites, there is a mere
overlap of 470 sites!
With this paper we have started an effort to evalu-
ate quality in search results in terms of two impor-
tant metrics, coverage and independence. We have
found that when searching controversial issues, both
of these quality metrics can be low. However, in high-
visibility queries the search engines may tune the re-
sults manually, as they have been criticized in the past
for not offering a balanced view.
We also found that Yahoo reports a much greater
number of back links than Google. Does it mean that
Yahoo sees a much larger portion of the web graph in
the neighborhood we were searching? This is rather
unlikely since the sites in the web neighborhoods we
examined seem to have a great interest to be seen. On
the other hand, there is a high degree of overlapping
results in the top-10 results in all queries, suggest-
ing the employment of very similar algorithms and
heuristics by the two search engines. One possible
explanation is that Google is filtering the back links
it is reporting. More research is needed to test these
conclusions, and we are currently working on it.
Queries on controversial issues will continue to
play an important role in web search. Search engines
need to be more sensitive in providing high quality
results that include high coverage and high indepen-
dence between results. Evolution of search results
over time is also important as it may reveal informa-
tion about the part of the web that is being manip-
ulated by spammers, activists and SEOs. Future re-
search will hopefully shed some light in this area.
Part of this work was supported by a Brachman-
Hoffman grant. We would like to thank Sociology
Professor Rosanna Hertz for providing expert evalua-
tion on the coverage of Q3.
Amento, B., Terveen, L., and Hill, W. (2000). Does au-
thority mean quality? Predicting expert quality ratings
of web documents. In Proceedings of the Twenty-
Third Annual International ACM SIGIR Conference
WEBIST 2008 - International Conference on Web Information Systems and Technologies
on Research and Development in Information Re-
trieval. ACM.
Berenson, A. (2000). On hair-trigger wall street, a stock
plunges on fake news. New York Times.
Brin, S. and Page, L. (1998). The anatomy of a large-scale
hypertextual Web search engine. Computer Networks
and ISDN Systems, 30(1–7):107–117.
Google (2003). The Google API, google, inc.
Graham, L. and Metaxas, P. T. (2003). “Of course it’s true;
i saw it on the internet!”: Critical thinking in the inter-
net era. Commun. ACM, 46(5):70–75.
Gyuongyi, Z. and Garcia-Molina, H. (2005). Web spam
taxonomy. In Proceedings of the First International
Workshop on Adversarial Information Retrieval on the
Web, Chiba, Japan.
Manning, C., Raghavan, P., and Schultze, H. (2008). Intro-
duction to Information Retrieval. Cambridge Press,
Cambridge, UK, (forthcoming) edition.
Metaxas, P. T. and Destefano, J. (2005). Web spam, propa-
ganda and trust. In Proceedings of the First Interna-
tional Workshop on Adversarial Information Retrieval
on the Web, Chiba, Japan.
Moran, M. and Hunt, B. (2006). Search Engine Marketing.
IBM Press, New Jersey, USA.
Ntoulas, A., Cho, J., and Olston, C. (2004). What’s new
on the web? the evolution of the web from a search
engine perspective. In Proceedings of the WWW 2004
Conference, New York, NY.
Silverstein, C., Marais, H., Henzinger, M., and Moricz, M.
(1999). Analysis of a very large web search engine
query log. SIGIR Forum, 33(1):6–12.
Vedder, A. (2001). Misinformation through the internet:
Epistemology and ethics. Intersentia, Antwerpen,
Gronigen, Oxford.
Yahoo (2006). The Yahoo search API, yahoo, inc.
COVERAGE AND INDEPENDENCE - Defining Quality in Web Search Results