{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T11:45:45Z","timestamp":1759146345291,"version":"3.41.0"},"reference-count":25,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T00:00:00Z","timestamp":1596067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000155","name":"Social Sciences and Humanities Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000155","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Manage. Inf. Syst."],"published-print":{"date-parts":[[2020,9,30]]},"abstract":"<jats:p>Untrustworthy content such as fake news and clickbait have become a pervasive problem on the Internet, causing significant socio-political problems around the world. Identifying untrustworthy content is a crucial step in countering them. The current best practices for identification involve content analysis and arduous fact-checking of the content. To complement content analysis, we propose examining websites\u2019 third-parties to identify their trustworthiness. Websites utilize third-parties, also known as their digital supply chains, to create and present content and help the website function. Third-parties are an important indication of a website's business model. Similar websites exhibit similarities in the third-parties they use. Using this perspective, we use machine learning and heuristic methods to discern similarities and dissimilarities in third-party usage, which we use to predict trustworthiness of websites. We demonstrate the effectiveness and robustness of our approach in predicting trustworthiness of websites from a database of News, Fake News, and Clickbait websites. Our approach can be easily and cost-effectively implemented to reinforce current identification methods.<\/jats:p>","DOI":"10.1145\/3382188","type":"journal-article","created":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T20:38:45Z","timestamp":1596141525000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Real or Not?"],"prefix":"10.1145","volume":"11","author":[{"given":"Ram D.","family":"Gopal","sequence":"first","affiliation":[{"name":"Warwick Business School, the University of Warwick, Coventry, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5443-7297","authenticated-orcid":false,"given":"Hooman","family":"Hidaji","sequence":"additional","affiliation":[{"name":"Haskayne School of Business, University of Calgary, Calgary, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sule Nur","family":"Kutlu","sequence":"additional","affiliation":[{"name":"School of Business, University of Connecticut, Storrs, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5408-6485","authenticated-orcid":false,"given":"Raymond A.","family":"Patterson","sequence":"additional","affiliation":[{"name":"Haskayne School of Business, University of Calgary, Calgary, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erik","family":"Rolland","sequence":"additional","affiliation":[{"name":"College of Business Administration, California State Polytechnic University, Pomona, CA, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitry","family":"Zhdanov","sequence":"additional","affiliation":[{"name":"J. Mack Robinson College of Business, Georgia State University, Atlanta, GA, U.S.A."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,7,30]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"N. Akpan. 2016. The Very Real Consequences of Fake News Stories and Why Your Brain Can't Ignore Them. Retrieved from http:\/\/www.pbs.org\/newshour\/updates\/real-consequences-fake-news-stories-brain-cant-ignore\/.  N. Akpan. 2016. The Very Real Consequences of Fake News Stories and Why Your Brain Can't Ignore Them. Retrieved from http:\/\/www.pbs.org\/newshour\/updates\/real-consequences-fake-news-stories-brain-cant-ignore\/."},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"A. Anand T. Chakraborty and N. Park. 2017. We used neural networks to detect clickbaits: You won't believe what happened next! In Proceedings of the European Conference on Information Retrieval. 541--547.  A. Anand T. Chakraborty and N. Park. 2017. We used neural networks to detect clickbaits: You won't believe what happened next! In Proceedings of the European Conference on Information Retrieval. 541--547.","DOI":"10.1007\/978-3-319-56608-5_46"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/2017470.2017473"},{"key":"e_1_2_1_4_1","first-page":"18","article-title":"How to master cross-enterprise collaboration","volume":"7","author":"Bowersox D.","year":"2003","unstructured":"D. Bowersox , D. Closs , and T. Stank . 2003 . How to master cross-enterprise collaboration . Supply Chain Manag. Rev. 7 , 4 (2003), 18 -- 27 . D. Bowersox, D. Closs, and T. Stank. 2003. How to master cross-enterprise collaboration. Supply Chain Manag. Rev. 7, 4 (2003), 18--27.","journal-title":"Supply Chain Manag. Rev."},{"volume-title":"Proceedings of the ACM Workshop on Multimodal Deception Detection. 15--19","author":"Chen Y.","key":"e_1_2_1_5_1","unstructured":"Y. Chen , N. J. Conroy , and V. L. Rubin . 2015. Misleading online content: Recognizing clickbait as false news . In Proceedings of the ACM Workshop on Multimodal Deception Detection. 15--19 . Y. Chen, N. J. Conroy, and V. L. Rubin. 2015. Misleading online content: Recognizing clickbait as false news. In Proceedings of the ACM Workshop on Multimodal Deception Detection. 15--19."},{"volume-title":"Proceedings of the IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM\u201916)","author":"Chakraborty A.","key":"e_1_2_1_6_1","unstructured":"A. Chakraborty , B. Paranjape , S. Kakarla , and N. Ganguly . 2016. Stop clickbait: Detecting and preventing clickbaits in online news media . In Proceedings of the IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM\u201916) . 9--16. A. Chakraborty, B. Paranjape, S. Kakarla, and N. Ganguly. 2016. Stop clickbait: Detecting and preventing clickbaits in online news media. In Proceedings of the IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM\u201916). 9--16."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1745-493X.2008.00069.x"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/1476127013478693"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2018\/13839"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-3723(12)70074-X"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1086\/228311"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10588-009-9060-8"},{"key":"e_1_2_1_13_1","unstructured":"K. Leetaru. 2016. Why Stopping \u201cFake\u201d News Is So Hard. Retrieved from https:\/\/www.forbes.com\/sites\/kalevleetaru\/2016\/11\/30\/why-stopping-fake-news-is-so-hard\/.  K. Leetaru. 2016. Why Stopping \u201cFake\u201d News Is So Hard. 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Zimdars, False, misleading, clickbait-y, and satirical \u201cnews\u201d sources. 2016. available at https:\/\/docs.google.com\/document\/u\/1\/d\/10eA5-mCZLSS4MQY5QGb5ewCbvb3VAL6pLkT53V_81ZyitM\/mobilebasic."}],"container-title":["ACM Transactions on Management Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3382188","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3382188","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:08Z","timestamp":1750197728000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3382188"}},"subtitle":["Identifying Untrustworthy News Websites Using Third-party Partnerships"],"short-title":[],"issued":{"date-parts":[[2020,7,30]]},"references-count":25,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,9,30]]}},"alternative-id":["10.1145\/3382188"],"URL":"https:\/\/doi.org\/10.1145\/3382188","relation":{},"ISSN":["2158-656X","2158-6578"],"issn-type":[{"type":"print","value":"2158-656X"},{"type":"electronic","value":"2158-6578"}],"subject":[],"published":{"date-parts":[[2020,7,30]]},"assertion":[{"value":"2019-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-07-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}