{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T19:15:51Z","timestamp":1789758951087,"version":"4.0.1"},"reference-count":123,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>\n                    Content recommender systems (RSs), which recommend content (e.g., news, videos) to users, can inadvertently facilitate the spread of misinformation (e.g., fake news, inaccurate content) due to their ignorance of content credibility. The widespread misinformation can lead to serious consequences, including public confusion and social unrest. Despite the pressing need to mitigate misinformation in content RSs, only a few studies have attempted to tackle this. Moreover, they generally require retraining the entire model from scratch, which is time-consuming and impractical for real-world applications. To this end, we propose\n                    <jats:italic toggle=\"yes\">MisEraser<\/jats:italic>\n                    , a novel misinformation unlearning framework that effectively mitigates misinformation in content RSs without retraining. Specifically, MisEraser consists of three key components: (1)\n                    <jats:italic toggle=\"yes\">A misinformation disentanglement network<\/jats:italic>\n                    , which effectively separates misinformation-specific information from item content while preserving misinformation-free information to maintain recommendation accuracy; (2)\n                    <jats:italic toggle=\"yes\">A misinformation fusion network<\/jats:italic>\n                    , which fuses misinformation-specific signals from both users\u2019 interaction sequences and the entire misinformation item set to enhance the comprehensiveness of misinformation unlearning; and (3)\n                    <jats:italic toggle=\"yes\">A misinformation erasing network<\/jats:italic>\n                    , which effectively removes the fused misinformation-specific signals from trained recommendation models, enabling them to generate misinformation-suppressed and more responsible recommendations. Extensive experiments demonstrate the effectiveness of MisEraser in mitigating misinformation while maintaining high recommendation accuracy and efficiency.\n                  <\/jats:p>","DOI":"10.1145\/3812649","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T15:59:46Z","timestamp":1777564786000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Misinformation Unlearning for Responsible Content Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1649-4094","authenticated-orcid":false,"given":"Zhuo","family":"Cai","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1133-9379","authenticated-orcid":false,"given":"Shoujin","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6763-5236","authenticated-orcid":false,"given":"Peilin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5344-1884","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8400-5754","authenticated-orcid":false,"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[{"name":"Deakin University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-9856","authenticated-orcid":false,"given":"Lianyong","family":"Qi","sequence":"additional","affiliation":[{"name":"China University of Petroleum East China, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0955-7588","authenticated-orcid":false,"given":"Julian","family":"McAuley","sequence":"additional","affiliation":[{"name":"University of California San Diego, La Jolla, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4698-8507","authenticated-orcid":false,"given":"Dietmar","family":"Jannach","sequence":"additional","affiliation":[{"name":"University of Klagenfurt, Klagenfurt am W\u00f6rthersee, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"issue":"11","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"5811","DOI":"10.1109\/TKDE.2024.3417232","article-title":"ContCommRTD: A distributed content-based misinformation-aware community detection system for real-time disaster reporting","volume":"36","author":"Apostol Elena-Simona","year":"2024","unstructured":"Elena-Simona Apostol, Ciprian-Octavian Truic\u0103, and Adrian Paschke. 2024. 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