%0 Conference Paper %1 dimitrov2017makes %A Dimitrov, Dimitar %A Singer, Philipp %A Lemmerich, Florian %A Strohmaier, Markus %B International Conference on World Wide Web %C Republic and Canton of Geneva, Switzerland %D 2017 %E Barrett, Rick %E Cummings, Rick %E Agichtein, Eugene %E Gabrilovich, Evgeniy %I International World Wide Web Conferences Steering Committee %K diss imported inthesis mixedtrails %P 917--926 %R 10.1145/3038912.3052613 %T What Makes a Link Successful on Wikipedia? %U https://doi.org/10.1145/3038912.3052613 %X While a plethora of hypertext links exist on the Web, only a small amount of them are regularly clicked. Starting from this observation, we set out to study large-scale click data from Wikipedia in order to understand what makes a link successful. We systematically analyze effects of link properties on the popularity of links. By utilizing mixed-effects hurdle models supplemented with descriptive insights, we find evidence of user preference towards links leading to the periphery of the network, towards links leading to semantically similar articles, and towards links in the top and left-side of the screen. We integrate these findings as Bayesian priors into a navigational Markov chain model and by doing so successfully improve the model fits. We further adapt and improve the well-known classic PageRank algorithm that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation. This work facilitates understanding navigational click behavior and thus can contribute to improving link structures and algorithms utilizing these structures. %@ 978-1-4503-4913-0

@inproceedings{dimitrov2017makes, abstract = {While a plethora of hypertext links exist on the Web, only a small amount of them are regularly clicked. Starting from this observation, we set out to study large-scale click data from Wikipedia in order to understand what makes a link successful. We systematically analyze effects of link properties on the popularity of links. By utilizing mixed-effects hurdle models supplemented with descriptive insights, we find evidence of user preference towards links leading to the periphery of the network, towards links leading to semantically similar articles, and towards links in the top and left-side of the screen. We integrate these findings as Bayesian priors into a navigational Markov chain model and by doing so successfully improve the model fits. We further adapt and improve the well-known classic PageRank algorithm that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation. This work facilitates understanding navigational click behavior and thus can contribute to improving link structures and algorithms utilizing these structures.}, acmid = {3052613}, added-at = {2017-06-21T17:16:57.000+0200}, address = {Republic and Canton of Geneva, Switzerland}, author = {Dimitrov, Dimitar and Singer, Philipp and Lemmerich, Florian and Strohmaier, Markus}, biburl = {https://www.bibsonomy.org/bibtex/2d9d79fa8e815eb6ecfc7d18752c7acfe/becker}, booktitle = {International Conference on World Wide Web}, description = {What Makes a Link Successful on Wikipedia?}, doi = {10.1145/3038912.3052613}, editor = {Barrett, Rick and Cummings, Rick and Agichtein, Eugene and Gabrilovich, Evgeniy}, interhash = {16bc28b4136c5b3dae55140ef802e90c}, intrahash = {d9d79fa8e815eb6ecfc7d18752c7acfe}, isbn = {978-1-4503-4913-0}, keywords = {diss imported inthesis mixedtrails}, location = {Perth, Australia}, numpages = {10}, pages = {917--926}, publisher = {International World Wide Web Conferences Steering Committee}, series = {WWW '17}, timestamp = {2017-12-20T16:56:19.000+0100}, title = {What Makes a Link Successful on Wikipedia?}, url = {https://doi.org/10.1145/3038912.3052613}, year = 2017 }