@inproceedings{conf/www/RokickiZS15, added-at = {2018-11-06T00:00:00.000+0100}, author = {Rokicki, Markus and Zerr, Sergej and Siersdorfer, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/29fc14e29012d8bdc28ef1b54ee3baf09/dblp}, booktitle = {WWW}, crossref = {conf/www/2015}, editor = {Gangemi, Aldo and Leonardi, Stefano and Panconesi, Alessandro}, ee = {https://doi.org/10.1145/2736277.2741097}, interhash = {2042d4bdac0f648081a15f911afade26}, intrahash = {9fc14e29012d8bdc28ef1b54ee3baf09}, isbn = {978-1-4503-3469-3}, keywords = {dblp}, pages = {906-915}, publisher = {ACM}, timestamp = {2018-11-07T15:38:50.000+0100}, title = {Groupsourcing: Team Competition Designs for Crowdsourcing.}, url = {http://dblp.uni-trier.de/db/conf/www/www2015.html#RokickiZS15}, year = 2015 } @inproceedings{Rokicki:2015:GTC:2736277.2741097, abstract = {Many data processing tasks such as semantic annotation of images, translation of texts in foreign languages, and labeling of training data for machine learning models require human input, and, on a large scale, can only be accurately solved using crowd based online work. Recent work shows that frameworks where crowd workers compete against each other can drastically reduce crowdsourcing costs, and outperform conventional reward schemes where the payment of online workers is proportional to the number of accomplished tasks ("pay-per-task"). In this paper, we investigate how team mechanisms can be leveraged to further improve the cost efficiency of crowdsourcing competitions. To this end, we introduce strategies for team based crowdsourcing, ranging from team formation processes where workers are randomly assigned to competing teams, over strategies involving self-organization where workers actively participate in team building, to combinations of team and individual competitions. Our large-scale experimental evaluation with more than 1,100 participants and overall 5,400 hours of work spent by crowd workers demonstrates that our team based crowdsourcing mechanisms are well accepted by online workers and lead to substantial performance boosts.}, acmid = {2741097}, added-at = {2016-10-18T15:11:55.000+0200}, address = {New York, NY, USA}, author = {Rokicki, Markus and Zerr, Sergej and Siersdorfer, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/217e7079f5700b1732a58521f718a7625/alexandriaproj}, booktitle = {Proceedings of the 24th International Conference on World Wide Web}, doi = {10.1145/2736277.2741097}, interhash = {2042d4bdac0f648081a15f911afade26}, intrahash = {17e7079f5700b1732a58521f718a7625}, isbn = {978-1-4503-3469-3}, keywords = {alexandria}, location = {Florence, Italy}, numpages = {10}, pages = {906--915}, publisher = {ACM}, series = {WWW '15}, timestamp = {2016-10-18T15:11:55.000+0200}, title = {Groupsourcing: Team Competition Designs for Crowdsourcing}, url = {http://doi.acm.org/10.1145/2736277.2741097}, year = 2015 } @inproceedings{DBLP:conf/www/RokickiZS15, abstract = {Many data processing tasks such as semantic annotation of images, translation of texts in foreign languages, and labeling of training data for machine learning models require human input, and, on a large scale, can only be accurately solved using crowd based online work. Recent work shows that frameworks where crowd workers compete against each other can drastically reduce crowdsourcing costs, and outperform conventional reward schemes where the payment of online workers is proportional to the number of accomplished tasks ("pay-per-task"). In this paper, we investigate how team mechanisms can be leveraged to further improve the cost efficiency of crowdsourcing competitions. To this end, we introduce strategies for team based crowdsourcing, ranging from team formation processes where workers are randomly assigned to competing teams, over strategies involving self-organization where workers actively participate in team building, to combinations of team and individual competitions. Our large-scale experimental evaluation with more than 1,100 participants and overall 5,400 hours of work spent by crowd workers demonstrates that our team based crowdsourcing mechanisms are well accepted by online workers and lead to substantial performance boosts.}, added-at = {2015-07-17T13:00:26.000+0200}, author = {Rokicki, Markus and Zerr, Sergej and Siersdorfer, Stefan}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {https://www.bibsonomy.org/bibtex/2363d52384cc1780e8ff09fd2811816e4/mrokicki}, booktitle = {Proceedings of the 24th International Conference on World Wide Web, {WWW} 2015, Florence, Italy, May 18-22, 2015}, crossref = {DBLP:conf/www/2015}, doi = {10.1145/2736277.2741097}, interhash = {2042d4bdac0f648081a15f911afade26}, intrahash = {363d52384cc1780e8ff09fd2811816e4}, keywords = {competition crowdsourcing groupsourcing myown team}, pages = {906--915}, publisher = {ACM}, timestamp = {2015-07-17T13:00:26.000+0200}, title = {Groupsourcing: Team Competition Designs for Crowdsourcing}, url = {http://doi.acm.org/10.1145/2736277.2741097}, year = 2015 } @inproceedings{DBLP:conf/www/RokickiZS15, added-at = {2015-05-27T11:10:12.000+0200}, author = {Rokicki, Markus and Zerr, Sergej and Siersdorfer, Stefan}, bibsource = {dblp computer science bibliography, http://dblp.org}, biburl = {https://www.bibsonomy.org/bibtex/2363d52384cc1780e8ff09fd2811816e4/zerr}, booktitle = {Proceedings of the 24th International Conference on World Wide Web, {WWW} 2015, Florence, Italy, May 18-22, 2015}, crossref = {DBLP:conf/www/2015}, doi = {10.1145/2736277.2741097}, interhash = {2042d4bdac0f648081a15f911afade26}, intrahash = {363d52384cc1780e8ff09fd2811816e4}, keywords = {alexandria competitions crowdsourcing dblp myown qualimaster}, pages = {906--915}, timestamp = {2015-07-16T17:45:01.000+0200}, title = {Groupsourcing: Team Competition Designs for Crowdsourcing}, url = {http://doi.acm.org/10.1145/2736277.2741097}, year = 2015 }