{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T22:14:36Z","timestamp":1762899276606},"reference-count":0,"publisher":"Walter de Gruyter GmbH","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,10,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Privacy policies are supposed to provide transparency about a service's data practices and help consumers make informed\nchoices about which services to entrust with their personal information. In practice, those privacy policies are typically\nlong and complex documents that are largely ignored by consumers. Even for regulators and data protection authorities\nprivacy policies are difficult to assess at scale. Crowdsourcing offers the potential to scale the analysis of privacy\npolicies with microtasks, for instance by assessing how specific data practices are addressed in privacy policies or\nextracting information about data practices of interest, which can then facilitate further analysis or be provided to\nusers in more effective notice formats. Crowdsourcing the analysis of complex privacy policy documents to non-expert\ncrowdworkers poses particular challenges. We discuss best practices, lessons learned and research challenges for\ncrowdsourcing privacy policy analysis.<\/jats:p>","DOI":"10.1515\/itit-2016-0009","type":"journal-article","created":{"date-parts":[[2016,6,27]],"date-time":"2016-06-27T16:14:52Z","timestamp":1467044092000},"page":"229-236","source":"Crossref","is-referenced-by-count":5,"title":["Crowdsourcing privacy policy analysis: Potential, challenges and best practices"],"prefix":"10.1515","volume":"58","author":[{"given":"Florian","family":"Schaub","sequence":"first","affiliation":[{"name":"Carnegie Mellon University, School of Computer Science, Pittsburgh, PA 15213, USA United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Travis D.","family":"Breaux","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, School of Computer Science, Pittsburgh, PA 15213, USA United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norman","family":"Sadeh","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, School of Computer Science, Pittsburgh, PA 15213, USA United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2016,6,24]]},"container-title":["it - Information Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/itit.2016.58.issue-5\/itit-2016-0009\/itit-2016-0009.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0009\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0009\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T11:44:50Z","timestamp":1624448690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2016-0009\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,6,24]]},"references-count":0,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2016,7,8]]},"published-print":{"date-parts":[[2016,10,28]]}},"alternative-id":["10.1515\/itit-2016-0009"],"URL":"https:\/\/doi.org\/10.1515\/itit-2016-0009","relation":{},"ISSN":["1611-2776","2196-7032"],"issn-type":[{"value":"1611-2776","type":"print"},{"value":"2196-7032","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,6,24]]}}}