Daniel Lichtnow
, Leandro Krug Wives
and José Palazzo Moreira de Oliveira
Instituto de Informática, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre-RS, Brazil
Centro Politécnico, Universidade Católica de Pelotas (UCPel), Pelotas-RS, Brazil
Keywords: Information Quality, Verifiability, Reliability, Measurement.
Abstract: This work presents an approach based on verifiability aspects to evaluate Web pages with textual content. In
the work, verifiability is related to the existence of references to information sources. In this sense, we take
into account that textual Web pages with references to information sources use to be better than Web pages
without references to information sources. Thus, aspects related to automatically identification of
verifiability indicators in textual Web pages are presented. For the given context, the results of preliminary
experiments show that verifiability aspects can be useful to infer the quality of texts present on the Web
addressed to Web users with little knowledge about a specific subject.
One considerable part of Web content consists of
textual content (e.g. blogs, articles, papers, etc.).
Although, some mechanisms have been created to
identify the quality of Web pages, the final quality
evaluation is a task that Web users must perform
Take into account this scenario; the present work
defines an approach to help in the evaluation process
of textual Web pages addressed to Web users with
little knowledge about a specific subject. The
proposal approach emphasizes the use of
verifiability quality indicators. Verifiability is
defined as “the degree and ease with which the
information can be checked for correctness”
(Naumann and Rolker, 2000). In textual Web pages,
verifiability is related to the existence of references
to information sources represent by Web links,
references to papers or even references to persons or
organizations. In the work we present some
preliminary experiments using verifiability
indicators to identify Web pages that contain urban
legends, myths, or rumors related to health
The paper is organized as follows. In section 2,
we present the related work. In section 3, we define
our approach. Section 4 describes some preliminary
experiments. Section 5 presents final remarks with
indications of future work.
Firstly, regarding to data/information quality, some
aspects should be highlighted:
There is no consensus among researchers about
which quality dimensions/factors must be considered
to measure or to represent data quality (Pernici and
Scannapieco, 2002);
The majority of data quality proposals are related
to structured data (Batini et al., 2009);
There are few works that emphasize data quality
in the context of Web (Batini et al., 2009).
In general, the quality of Web pages is measured
considering the link structure present on the Web
(Brin and Page, 1998). For instance, link-based
quality indicators are evaluated in some works
(Amento et al., 2000).
Beyond Web links, Zhu and Gauch (2000)
consider other quality metrics like currency (the time
stamp of the last modification of the document), the
ratio between the number of broken links on a page
by the total number of links, information-to-noise
(the ratio between the number of tokens present in
Lichtnow D., Krug Wives L. and Palazzo Moreira de Oliveira J..
DOI: 10.5220/0003935906890694
In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 689-694
ISBN: 978-989-8565-08-2
2012 SCITEPRESS (Science and Technology Publications, Lda.)
the pre-processed main content by the number of
tokens of the document) and, popularity (the number
of inlinks – inlinks are the number of Web links
pointing to Web page). In this work, the best results
were obtained with information-to-noise metric.
In (Bethard et al., 2009), twelve dimensions of
quality related to specific educational purposes are
identified. For each quality dimension, some quality
indicators are identified. In that work, the approach
to identify quality indicators consists on using a
training corpus where these indicators are previously
annotated by reviewers (the indicators consist on
word sequences, for instance). The process of
quality identification consists in using Machine
Learning techniques to predict whether a resource
has good quality (contain indicators).
In addition, there are works in which the
objective is to evaluate a specific type of
information on the Web. One example is (Dalip et
al., 2009), where the quality evaluation of
Wikipedia’s articles considers features like reviews
per day. The problem is that some of these features
are limited to Wikipedia’s articles.
In (Denecke and Nejdl, 2009), the aim is to
identify if a text in a Web page, related to health
issues, is informative or affective. The authors
consider that informative content has more value and
uses Natural Language Processing techniques to
identify this fact.
In (Yin et al., 2007) the authors try to identify the
most reliable Web page by comparing the content
(structured data extracted from Web page) of Web
pages. The process uses an iterative method, where
the data present in Web pages (e.g. year of
publication) are compared and the most reliable
source (Web page) is identified.
It is possible to identify a set of quality indicators
to evaluate textual Web pages. Each quality
indicator/metric must be assigned to a specific
quality dimension that emphasizes a distinct quality
aspect. Regarding to quality dimensions, we follow
the definitions of (Wang and Strong, 1996)
(Naumann and Rolker, 1999) and (Naumann and
Rolker, 2000). Bellow, we present some quality
dimensions and quality indicators/metrics.
Accuracy. Spelling errors (Batini et al., 2008);
number of pages in a Web site (indicates how much
effort the author is devoting to the site, and more
effort tends to indicate higher quality) (Amento et
al., 2000); comparison of data with a reliable source
(Yin et al., 2007); information-to-noise evaluation
(Zhu and Gauch, 2000);
Believability. PageRank (Brin and Page, 1998);
inlinks (Amento et al., 2000); qualifications of the
author or provider of the page (HONCODE, 2009).
Timeliness. Creation date; last update.
Relevance. Cosine (Salton et al., 1975) and
metadata related to subject (Naumann and Rolker,
Verifiability. References to information sources
(e.g. Web links, references to papers or even to
persons or organizations) (Naumann and Rolker,
These indicators must be considered heuristics,
because since it may be difficult to evaluate some of
these aspects (i.e., accuracy). We note that quality
indicators related to verifiability are almost ignored
in the related works. We also verify that bad textual
Web pages (in general) do not indicate information
sources. Besides, verifiability is an important quality
criterion for Wikipedia
. Taking into account these
facts, we define our approach in the next section.
In this section, we present the proposal approach to
evaluate the content quality of textual Web pages
using verifiability indicators. Initially, considering
that quality means “fitness for use” (Wang and
Strong, 1996), the context of use for our approach is
defined. After, we describe a scenario of use for our
approach. We also discuss how to identify some
types of source references in the textual Web pages.
3.1 The Context of Use
For defining the context of use, the start point of our
approach is related to Web search goals. A Web
search goal is related to a user query, i.e., users
construct queries to express his/her or her needs
related to some task. A relevant taxonomy of Web
search goals is presented in (Rose and Levinson,
2004). Another important aspect related to context
of use is the user profile. Thus, based on (Garzotto et
al., 1997), we define three types of Web users:
Casual (user does not have knowledge about the
subject); Intentional (user has some knowledge, or at
least a significant interest and Specialist (user has a
lot of knowledge on the subject).
In the proposed approach, we focus on Casual or
Intentional Web users with the following Web
search goals: Open (direct) or Advice (Rose and
Levinson, 2004). In this sense, the experiments we
have conducted (described in section 4) are related
to getting simple textual information about a specific
subject: health. Our motivation is related to the fact
that there are a set of urban legends, myths, or
rumours related to health on the Web, addressed to
Web users with little knowledge about medicine.
3.2 Scenario
The aim of the proposal approach is to provide
information about the verifiability degree of a Web
page. This information gives more subsidies to users
so they can better judge the information quality.
Besides, they can be used to re-rank the results
provided by search engines. In this sense, the
proposed approach complements the analysis
provided by other quality indicators like, for
instance, link structure analysis.
Thus, the approach can be used in a tool (e.g. an
extension of a Web Browser) that receives an
information request (a set of URL’s of Web pages
returned by a search engine) and determines the
degree of verifiability of each Web page. Details
about the implementation of this tool are beyond of
the scope of this paper, but, briefly, some aspects of
the implementation follows the considerations stated
in Section 3.3.
3.3 Identifying References
Figure 1 gives an overview of the reference’s
extracting process. Since a Web page may contain
other types of content (e.g., user comments,
advertising, legal disclaimers, etc.) the first step is to
identify where its main content is. In this sense,
some works address the main content extraction
problem (Kohlschütter and Nejdl, 2008) (Kato et al.,
Figure 1: Reference’s identification process.
The next step is to extract the references
(hyperlinks, references to persons and bibliographic
sources). Obviously, the process to identify
hyperlinks on the main content is easier than the
identification of references to papers or people
because it is possible to identify Web links by
HTML tags.
In the case of references to papers, when there
are no specific links to them, it is necessary to
identify where the references are. In this sense,
some heuristics consider expressions containing the
following key words: references; selected
references; see also; further reading on the subject;
to learn more about; additional reading; find out
more about; more information; etc. We have
observed that references are frequently placed after
the main content.
For identifying these references, one possibility
is to use a method similar to the one used in (Kato et
al., 2008) for identifying the name of the author in a
Web page. Besides, we have found that in online
newspaper articles available in Web pages, in
general, there are no references to papers or Web
links. Then, references to persons must be
For instance, in paragraphs (1) and (2), we
present two texts samples regarding this problem.
Text (2) has more verifiability than (1) because the
affiliation is mentioned.
“[...] Several years ago, I learned of the discovery of
Richard R. Vensal, D.D.S. that asparagus might cure
“[...] ‘The results [...] can be exploited for cancer
therapy,’ says Dario Altieri, director of the University
of Massachusetts Cancer Center in Worcester[...]”
The degree of complexity to identify persons is
higher than to identify references to papers or Web
links in a Web page.
For identifying references to people as an
information source, we use a Named Entity
Recognizer - NER (Finkel et al., 2005). The process
output is shown in (3).
"’The results […] can be exploited for cancer therapy,’
says <PERSON>Dario Altieri</PERSON>, director of
the <ORGANIZATION>University of Massachusetts
Worcester </LOCATION>.”
We consider persons as information sources only
when the affiliation is mentioned. For identifying
affiliation, we define a set of rules in a grammar
constructed with JavaCC. Thus, the affiliation is
identified by expressions like (4) where NE is a
Named Entity.
NE<Person> "of the" NE<Organization>
NE<Person> "director of the" NE<Organization>
NE<Person> "a"+ (("a" - "z"))+ "at the"
After, we check the affiliation on the Web. To do
this, we use Google API
, querying the affiliation
name (the aim is to identify the Web site of the
organization). This process is easy because, in
general, Google returns the site of the organization
as the first result. Then, a new query is submitted
using the person name and the Web site of the
organization as argument (e.g., see 5 bellow).
“John Smith”
When the query (5) does not return any result, we
consider that the degree of verifiability is low.
Considering the text examples presented before (1
and 2), it was possible to identify references to
Dario Altieri on the Web site of University of
Massachusetts Cancer Center. In the case of
Richard R. Vensal there was no information about
his affiliation, thus text (1) was considered as having
low verifiability.
As shown in Figure 1, the degree of verifiability
of each text is determined in the end of the process.
At this moment, we consider that a Web page with
any type of reference (according to our quality
criteria – see section 4) is verifiable. In the future,
we will improve this criterion.
We conducted some preliminary experiments to
evaluate if references could be useful to determine
the quality of the content of textual Web pages.
4.1 Experiment 1
In a first experiment, we have selected 50 Web
pages associated to health related themes. From
these, 25 can be considered as good (i.e., verifiable)
and 25 as bad (not verifiable). The 25 bad Web
pages contain known urban legends, myths, or
rumours (identified in
). The good Web
pages were selected from Health related Web sites
(e.g., MedlinePlus
The results (Table 1) indicate that only 16% of
the good Web pages do not have references to some
kind of source. In the case of bad Web pages, 9 Web
pages have some kind of reference, but only 3 Web
pages (12%) have good references, following our
Table 1: Web pages with and without references.
Category Total
Web pages
Web pages
25 19 2 4
25 6 3 16
4.2 Experiment 2
In another experiment, we collected the first 30 Web
pages returned by Google and used them in the
experiment. We considered that a Web user tends to
view, in general, only the first Web pages returned
by a Web search engine (Hawking et al., 2001).
We decided to focus on the context of cure of
cancer based on asparagus, since it is a known
myth/rumour (according to
). To
retrieve Web pages, the query was: asparagus
cancer cure. We only considered Web pages
containing textual content and discarded the ones in
which the content was a video or e-mail. Regarding
the user profile, we selected Web pages
appropriately, to users with little knowledge about
medicine (casual or intentional user).
The Web pages returned by the search engine
were manually evaluated and classified as Good (do
not support the myth/rumour), Medium (mentioned
the myth/rumour but express doubts about) or Bad
(support the myth/rumour). Table 2 shows this
Table 2: Search results.
Category Number of Web pages
Good 6
Medium 8
Bad 16
Using the Web pages selected, we evaluated
distinct quality indicators (Cosine, information-to-
noise, inlinks to Web page, size of the site and
references to sources).
After extracting the main content from Web
pages (manually), we have used Google API
obtain the number of inlinks and the number of Web
pages in a Web site. The cosine was calculated
following Salton et al.’s definition (Salton et al.,
1975), using the main content of each Web page and
the query “asparagus cure cancer”. We calculated
the information-to-noise by dividing the number of
words in the main content by the total number of
words in the Web page.
Regarding verifiability, we analyzed the presence
or absence of references to sources related to the
main content. In this case, part of the process was
manually performed (the identification of references
to papers). For identifying references to persons, we
used a Named Entity Recognition - NER (Finkel et
al., 2005).
We considered the following types of references
as quality indicators:
Links to Web pages of others Web sites;
References to papers;
References to persons.
For each type of reference, we considered the
following quality criteria:
A Web link must point to a good Web page. In
our approach, a good Web page is the one that
belongs to Web sites finished by .gov or .edu, or a
Web pages with good references;
A paper related to a reference must exist on the
Web (must be indexed by Scholar Google);
A reference to a person must contain the
complete name of the person and its affiliation (e.g.
John Smith of NASA).
When a Web page has a good reference (following
our criteria), we assign the value 1 to the degree of
verifiability. In another hand, we assign the value 0
to the degree of verifiability when a Web page does
not have any good reference.
The Table 3 contains the results of this
experiment. For each metric we generated a ranking
of textual Web pages and computed precision at 5.
Precision indicates how many good Web pages
appear near the top (precision of 0.20 at 5 means that
1 of top 5 are good Web pages).
Table 3: Experiment 2 - results.
Quality Indicator Precision at 5
Original Ranking 0.4
Cosine 0.2
Information-to-noise 0.4
Inlinks to Web page 0.2
Size of site 0.2
References to sources 0.6
The best results were obtained using references
to sources. Besides, this preliminary experiment
shows that metrics like number of pages in a Web
site (the size of Web site), which were considered
useful in some of the previous work (Section 2),
now, apparently, were not so useful in our case. In
the case of the size of Web site, the problem is that
some Web sites are Web applications that allow to
any Web user to publish content. Thus, the number
of Web pages does not represent how much effort
the author is devoting to the Web site (Amento et al.,
This work presented an approach that is based on
verifiability to evaluate the textual Web page’s
content quality. Considering the results of the
preliminary experiments performed, we consider
that the use of these references is promising.
There are some difficulties to extract references
from textual Web pages (e.g. identification of main
content, person names, homonymous, name
variations, check if the subject of a source is related
to Web page that makes reference to this source,
etc.). In this sense, we mentioned some works that
can help with some of these problems, and we intend
to apply some of these techniques in a more
effective way.
Another future work consists on distinguishing
the verifiability degree of Web pages. Besides, we
are going to define how to combine metrics based on
verifiability with other metrics. We also intend to do
more experiments.
One limitation of the approach is that the author
of Web content can include good references to
increase the trustworthiness of bad Web pages. In
this sense, we note that, in general, bad textual Web
pages do not provide any good reference. Besides,
one possibility is to give to users an explanation
about the references, identified by the approach on
the textual Web page. How to produce this
explanation is a future work.
This work is partially supported by CNPq, Conselho
Nacional de Desenvolvimento Científico e
Tecnológico, Brazil and CAPES, Coordenação de
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