Difference between revisions of "To Link or Not to Link: Ranking Hyperlinks in Wikipedia Using Collective Attention"
(Adding new article - To Link or Not to Link: Ranking Hyperlinks in Wikipedia Using Collective Attention) |
(Links) |
||
| Line 1: | Line 1: | ||
| − | '''To Link or Not to Link: Ranking Hyperlinks in Wikipedia Using Collective Attention''' - scientific work related to Wikipedia quality published in 2016, written by Philip Thruesen, Jaroslav Cechak, Blandine Seznec, Roel Castalio and Nattiya Kanhabua. | + | '''To Link or Not to Link: Ranking Hyperlinks in Wikipedia Using Collective Attention''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Philip Thruesen]], [[Jaroslav Cechak]], [[Blandine Seznec]], [[Roel Castalio]] and [[Nattiya Kanhabua]]. |
== Overview == | == Overview == | ||
| − | Wikipedia is one of the fastest growing websites and a primary source of knowledge on the Internet. Being a wiki, its content is crowd-sourced by the users. This has many benefits and it is one of the main reasons it has grown to reach more than 5 million articles in its English version. Nevertheless, this also raises issues, like the overlinking of articles, which are difficult to deal with by editors. In this paper, authors tackle overlinking in Wikipedia as a ranking problem. Authors apply Learning to Rank algorithms to evaluate the click frequency of links in an effort to distinguish the most useful links for users. To accomplish this, authors develop a ground truth, which serves as baseline for algorithm and compare hyperlink features to implement the most advantageous ones. The results show 86.2% accuracy with the top-6 most useful features and 87.7% accuracy with the complete feature set. Considering these results, authors outline a solution to the overlinking problem. By removing the most inadequate links, authors suggest that readability of Wikipedia articles could be improved while preserving most of its useful links. | + | Wikipedia is one of the fastest growing websites and a primary source of knowledge on the Internet. Being a wiki, its content is crowd-sourced by the users. This has many benefits and it is one of the main reasons it has grown to reach more than 5 million articles in its English version. Nevertheless, this also raises issues, like the overlinking of articles, which are difficult to deal with by editors. In this paper, authors tackle overlinking in [[Wikipedia]] as a ranking problem. Authors apply Learning to Rank algorithms to evaluate the click frequency of links in an effort to distinguish the most useful links for users. To accomplish this, authors develop a ground truth, which serves as baseline for algorithm and compare hyperlink [[features]] to implement the most advantageous ones. The results show 86.2% accuracy with the top-6 most useful features and 87.7% accuracy with the complete feature set. Considering these results, authors outline a solution to the overlinking problem. By removing the most inadequate links, authors suggest that [[readability]] of Wikipedia articles could be improved while preserving most of its useful links. |
Revision as of 09:35, 20 October 2019
To Link or Not to Link: Ranking Hyperlinks in Wikipedia Using Collective Attention - scientific work related to Wikipedia quality published in 2016, written by Philip Thruesen, Jaroslav Cechak, Blandine Seznec, Roel Castalio and Nattiya Kanhabua.
Overview
Wikipedia is one of the fastest growing websites and a primary source of knowledge on the Internet. Being a wiki, its content is crowd-sourced by the users. This has many benefits and it is one of the main reasons it has grown to reach more than 5 million articles in its English version. Nevertheless, this also raises issues, like the overlinking of articles, which are difficult to deal with by editors. In this paper, authors tackle overlinking in Wikipedia as a ranking problem. Authors apply Learning to Rank algorithms to evaluate the click frequency of links in an effort to distinguish the most useful links for users. To accomplish this, authors develop a ground truth, which serves as baseline for algorithm and compare hyperlink features to implement the most advantageous ones. The results show 86.2% accuracy with the top-6 most useful features and 87.7% accuracy with the complete feature set. Considering these results, authors outline a solution to the overlinking problem. By removing the most inadequate links, authors suggest that readability of Wikipedia articles could be improved while preserving most of its useful links.
