{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:00:28Z","timestamp":1774540828326,"version":"3.50.1"},"reference-count":37,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2014,6,1]],"date-time":"2014-06-01T00:00:00Z","timestamp":1401580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee, Hong Kong","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004602","name":"Program for New Century Excellent Talents in University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2014,6]]},"abstract":"<jats:p>Semi-automatic anti-spam algorithms propagate either trust through links from a good seed set (e.g., TrustRank) or distrust through inverse links from a bad seed set (e.g., Anti-TrustRank) to the entire Web. These kinds of algorithms have shown their powers in combating link-based Web spam since they integrate both human judgement and machine intelligence. Nevertheless, there is still much space for improvement. One issue of most existing trust\/distust propagation algorithms is that only trust or distrust is propagated and only a good seed set or a bad seed set is used. According to Wu et al. [2006a], a combined usage of both trust and distrust propagation can lead to better results, and an effective framework is needed to realize this insight. Another more serious issue of existing algorithms is that trust or distrust is propagated in nondifferential ways, that is, a page propagates its trust or distrust score uniformly to its neighbors, without considering whether each neighbor should be trusted or distrusted. Such kinds of blind propagating schemes are inconsistent with the original intention of trust\/distrust propagation. However, it seems impossible to implement differential propagation if only trust or distrust is propagated. In this article, we take the view that each Web page has both a trustworthy side and an untrustworthy side, and we thusly assign two scores to each Web page: T-Rank, scoring the trustworthiness of the page, and D-Rank, scoring the untrustworthiness of the page. We then propose an integrated framework that propagates both trust and distrust. In the framework, the propagation of T-Rank\/D-Rank is penalized by the target's current D-Rank\/T-Rank. In other words, the propagation of T-Rank\/D-Rank is decided by the target's current (generalized) probability of being trustworthy\/untrustworthy; thus a page propagates more trust\/distrust to a trustworthy\/untrustworthy neighbor than to an untrustworthy\/trustworthy neighbor. In this way, propagating both trust and distrust with target differentiation is implemented. We use T-Rank scores to realize spam demotion and D-Rank scores to accomplish spam detection. The proposed Trust-DistrustRank (TDR) algorithm regresses to TrustRank and Anti-TrustRank when the penalty factor is set to 1 and 0, respectively. Thus TDR could be seen as a combinatorial generalization of both TrustRank and Anti-TrustRank. TDR not only makes full use of both trust and distrust propagation, but also overcomes the disadvantages of both TrustRank and Anti-TrustRank. Experimental results on benchmark datasets show that TDR outperforms other semi-automatic anti-spam algorithms for both spam demotion and spam detection tasks under various criteria.<\/jats:p>","DOI":"10.1145\/2628440","type":"journal-article","created":{"date-parts":[[2014,7,7]],"date-time":"2014-07-07T11:55:18Z","timestamp":1404734118000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Propagating Both Trust and Distrust with Target Differentiation for Combating Link-Based Web Spam"],"prefix":"10.1145","volume":"8","author":[{"given":"Xianchao","family":"Zhang","sequence":"first","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Wang","sequence":"additional","affiliation":[{"name":"Tencent Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Mou","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxin","family":"Liang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,7,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1326561.1326563"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the Workshop on Web Mining and Web Usage Analysis (WebKDD'06)","author":"Becchetti L.","unstructured":"L. Becchetti , C. Castillo , D. Donato , S. Leonardi , and R. Baeza-Yates . 2006. Using rank propagation and probabilistic counting for link-based spam detection . In Proceedings of the Workshop on Web Mining and Web Usage Analysis (WebKDD'06) . ACM Press, New York. L. Becchetti, C. Castillo, D. Donato, S. Leonardi, and R. Baeza-Yates. 2006. Using rank propagation and probabilistic counting for link-based spam detection. In Proceedings of the Workshop on Web Mining and Web Usage Analysis (WebKDD'06). ACM Press, New York."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb'05)","author":"Benczur A. A.","unstructured":"A. A. Benczur , K. Csalogany , T. Sarlos , and M. Uher . 2005. Spamrank -- Fully automatic link spam detection . In Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb'05) . 25--38. A. A. Benczur, K. Csalogany, T. Sarlos, and M. Uher. 2005. Spamrank -- Fully automatic link spam detection. In Proceedings of the 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb'05). 25--38."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1062745.1062787"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"e_1_2_1_6_1","unstructured":"J. Callan M. Hoy C. Yoo and L. Zhao. 2009. The clueweb09 data set. http:\/\/boston.lti.cs.cmu.edu\/Data\/clueweb09\/.  J. Callan M. Hoy C. Yoo and L. Zhao. 2009. 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