Trust Problem in the Link-Based Popularity Ranking
The idea behind the popularity ranking algorithms is that, by linking to a page, you imply that that page deserves attention.
Search engines use links to determine the authority of pages in topics described by the link anchor text.
The problem is that every link is considered as a positive endorsement with no regard to the real intention of the linking person.
There is no effective way for a search engine to distinguish between positive and negative endorsements in links yet.
The earliest web-like development by Tim Berners-Lee - the Enquire program – allowed the usage of various types of relationships between documents, such as similar-to, part-of, or made-by.
However in his later work on the World Wide Web and HTML Tim Berners-Lee has abandoned multiple-types of relationships for simplicity.
Flat links with no relationship information have been used ever since.
This article was originally published at Trust Problem in the Link-Based Popularity Ranking.
Link Abuse The absence of this feature has been widely exploited by people trying to manipulate search engines results since the introduction of the link-based popularity ranking.
The most notable examples are link farms, which used to flourish just a couple years ago.
Search engines had realized the problem of abuse and started penalizing link farms and sites that link to them.
But still with the anchor text and the host page’s own authority being the only parameters used to evaluate the link impact, there exist many other ways to manipulate rankings.
By using anchor text large web-users communities can perform the infamous Google-bombing:see ‘miserable failure’ search results in Google.
And this can be employed against other search engines as well, e.
g.
‘друг народа’ (‘people’s friend’) query in the Russian major search engine Yandex.
ru returns V.
Putin’s bio on the top of the results Adding Semantics Info into Link Markup The problem lies in the very nature of HTML.
Designed as a document structure markup language it is ineffective for describing the semantics of a document.
Some extensions had to be introduced into the link markup to make it able to reflect the nature of the relationship to the linked document.
One example is the rev attribute.
This attribute is used in the VoteLinks microformat proposed by the Technorati.
com to allow users to express their opinion about linked blogs.
The rev attribute in the VoteLinks format can have the following values: ‘vote-for’, ‘vote-abstain’ and ‘vote-against’, thus reflecting positive, neutral and negative opinions.
This makes it possible to augment or reduce the authority of the linked document respectively.
The rel attributes values such as ‘friend’, ‘colleague’, ‘sibling’, ‘neighbor’ or ‘spouse’ allow a different approach, reflecting the nature of the relationships between two documents, rather than the opinion.
This approach is also used by Technorati.
com and might have certain value within a community of bloggers.
None of these approaches is currently used by any major search engine.
The rel attribute value ‘nofollow’ is the only one to be taken into account by Google, MSN and Yahoo.
This value is intended to show that the linking person is not certain about his approval of the linked document.
The search engine crawlers are supposed not to follow such links, but many webmasters report that actually they still do.
However it is possible that such links do not propagate authority.
Possible Impact of Trust Links What if search engines were able to determine the trust or distrust nature of a link? Would the existing ranking be significantly different? Currently inbound links show the measure of attention to a certain page.
Therefore even those pages which are linked to express distrust or even disgust might be ranked higher than positively endorsed pages for the same query.
There is another approach that would have to be reconsidered.
Now if page A links to page B, it propagates a fraction of its authority value to B.
If there is a reciprocal link, some authority can be returned to A, so that both pages benefit from the mutual trust.
But consider now that a highly ranked page A distrusts page B.
Page B also adds a distrust link to A in revenge.
Should we decrease the ranking of A because a distrusted page B also negatively links to it? Or should we stop considering any opinion from B? To determine the effect of the search engines’ ability to distinguish between positive and negative opinions in web links Paolo Massa and Conor Hayes have conducted an experiment using the linkage data from the Epinions.
com website.
Epinions.
com is a web community where users can write their opinions on movies, books, goods and services.
It is also possible to rate to use other users’ reviews, thus expressing trust or distrust.
In the experiment’s model, users are considered as web pages, while their ratings are seen as incoming links.
The 20 top ranked users were sorted by their total number of ratings (positive and negative) and by the number of the positive rankings only.
The total number of rankings shows the attention to the user, which is similar to the link popularity of a web page.
Using statistical methods P.
Massa and C.
Hayes have determined the alignment factor between the attention ranking and the trust ranking.
The value is 0.
8 and it should be noted that since people tend to assign positive ranking more frequently than negative (often a disappointed user would simply not rate ate at all) – the actual alignment value can be even less.
However trust linking would be ineffective in the web.
The introduction of trust/distrust linking would create wide opportunities for abuse, and not just from gray spammers.
Any highly-ranked website would be able to destroy authority of any less ranked competitor with a power of a single link! While in relatively small communities of bloggers (Technorati.
com) or reviewers (Epinions.
com) such wars can be prevented by human moderation, the existing technology still doesn’t allow search engines to perform a similar task in the Web.
References:
Search engines use links to determine the authority of pages in topics described by the link anchor text.
The problem is that every link is considered as a positive endorsement with no regard to the real intention of the linking person.
There is no effective way for a search engine to distinguish between positive and negative endorsements in links yet.
The earliest web-like development by Tim Berners-Lee - the Enquire program – allowed the usage of various types of relationships between documents, such as similar-to, part-of, or made-by.
However in his later work on the World Wide Web and HTML Tim Berners-Lee has abandoned multiple-types of relationships for simplicity.
Flat links with no relationship information have been used ever since.
This article was originally published at Trust Problem in the Link-Based Popularity Ranking.
Link Abuse The absence of this feature has been widely exploited by people trying to manipulate search engines results since the introduction of the link-based popularity ranking.
The most notable examples are link farms, which used to flourish just a couple years ago.
Search engines had realized the problem of abuse and started penalizing link farms and sites that link to them.
But still with the anchor text and the host page’s own authority being the only parameters used to evaluate the link impact, there exist many other ways to manipulate rankings.
By using anchor text large web-users communities can perform the infamous Google-bombing:see ‘miserable failure’ search results in Google.
And this can be employed against other search engines as well, e.
g.
‘друг народа’ (‘people’s friend’) query in the Russian major search engine Yandex.
ru returns V.
Putin’s bio on the top of the results Adding Semantics Info into Link Markup The problem lies in the very nature of HTML.
Designed as a document structure markup language it is ineffective for describing the semantics of a document.
Some extensions had to be introduced into the link markup to make it able to reflect the nature of the relationship to the linked document.
One example is the rev attribute.
This attribute is used in the VoteLinks microformat proposed by the Technorati.
com to allow users to express their opinion about linked blogs.
The rev attribute in the VoteLinks format can have the following values: ‘vote-for’, ‘vote-abstain’ and ‘vote-against’, thus reflecting positive, neutral and negative opinions.
This makes it possible to augment or reduce the authority of the linked document respectively.
The rel attributes values such as ‘friend’, ‘colleague’, ‘sibling’, ‘neighbor’ or ‘spouse’ allow a different approach, reflecting the nature of the relationships between two documents, rather than the opinion.
This approach is also used by Technorati.
com and might have certain value within a community of bloggers.
None of these approaches is currently used by any major search engine.
The rel attribute value ‘nofollow’ is the only one to be taken into account by Google, MSN and Yahoo.
This value is intended to show that the linking person is not certain about his approval of the linked document.
The search engine crawlers are supposed not to follow such links, but many webmasters report that actually they still do.
However it is possible that such links do not propagate authority.
Possible Impact of Trust Links What if search engines were able to determine the trust or distrust nature of a link? Would the existing ranking be significantly different? Currently inbound links show the measure of attention to a certain page.
Therefore even those pages which are linked to express distrust or even disgust might be ranked higher than positively endorsed pages for the same query.
There is another approach that would have to be reconsidered.
Now if page A links to page B, it propagates a fraction of its authority value to B.
If there is a reciprocal link, some authority can be returned to A, so that both pages benefit from the mutual trust.
But consider now that a highly ranked page A distrusts page B.
Page B also adds a distrust link to A in revenge.
Should we decrease the ranking of A because a distrusted page B also negatively links to it? Or should we stop considering any opinion from B? To determine the effect of the search engines’ ability to distinguish between positive and negative opinions in web links Paolo Massa and Conor Hayes have conducted an experiment using the linkage data from the Epinions.
com website.
Epinions.
com is a web community where users can write their opinions on movies, books, goods and services.
It is also possible to rate to use other users’ reviews, thus expressing trust or distrust.
In the experiment’s model, users are considered as web pages, while their ratings are seen as incoming links.
The 20 top ranked users were sorted by their total number of ratings (positive and negative) and by the number of the positive rankings only.
The total number of rankings shows the attention to the user, which is similar to the link popularity of a web page.
Using statistical methods P.
Massa and C.
Hayes have determined the alignment factor between the attention ranking and the trust ranking.
The value is 0.
8 and it should be noted that since people tend to assign positive ranking more frequently than negative (often a disappointed user would simply not rate ate at all) – the actual alignment value can be even less.
However trust linking would be ineffective in the web.
The introduction of trust/distrust linking would create wide opportunities for abuse, and not just from gray spammers.
Any highly-ranked website would be able to destroy authority of any less ranked competitor with a power of a single link! While in relatively small communities of bloggers (Technorati.
com) or reviewers (Epinions.
com) such wars can be prevented by human moderation, the existing technology still doesn’t allow search engines to perform a similar task in the Web.
References:
- Massa, P.
and Hayes, C.
2005, ‘Page-reRank: using trusted links to re-rank authority’.
Istituto Trentino di Cultura. - Ntoulas, A.
, Cho, J.
, Cho, H.
K.
, et al.
2004, ‘A Study On The Evolution Of The Web’.
University of California.
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