{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T03:54:37Z","timestamp":1779249277019,"version":"3.51.4"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T00:00:00Z","timestamp":1647648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Commun. ACM"],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:p>We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection. Unlike previous contextual models that have targeted performance, our focus is on representation learning. Compared to transductive models, FANG is scalable in training as it does not have to maintain the social entities involved in the propagation of other news and is efficient at inference time, without the need to reprocess the entire graph. Our experimental results show that FANG is better at capturing the social context into a high-fidelity representation, compared to recent graphical and nongraphical models. In particular, FANG yields significant improvements for the task of fake news detection and is robust in the case of limited training data. We further demonstrate that the representations learned by FANG generalize to related tasks, such as predicting the factuality of reporting of a news medium.<\/jats:p>","DOI":"10.1145\/3517214","type":"journal-article","created":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T16:24:32Z","timestamp":1647707072000},"page":"124-132","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["FANG"],"prefix":"10.1145","volume":"65","author":[{"given":"Van-Hoang","family":"Nguyen","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazunari","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"Kyoto University, Kyoto, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Preslav","family":"Nakov","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, HBKU, Doha, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min-Yen","family":"Kan","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,19]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/304181.304187"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357994"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.91"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/3016100.3016318"},{"key":"e_1_2_1_7_1","volume-title":"5th International Conference on Learning Representations, ICLR 2017 (Toulon, France, April 24--26, 2017). 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In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI'18\/IAAI'18\/EAAI'18). AAAI Press Article 44 2018 354--361.  Liu Y. Wu Y.-F.B. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI'18\/IAAI'18\/EAAI'18). 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Fake news Detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673 (2019)."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983661"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3055133"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132877"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/576628"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1089\/big.2020.0062"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3137597.3137600"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290994"},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018","author":"Thorne J.","year":"2018","unstructured":"Thorne , J. , Vlachos , A. Automated fact checking: task formulations, methods and future directions. In E.M. Bender, L. Derczynski, P. Isabelle, eds . Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018 ( Santa Fe, New Mexico, USA, August 20--26 , 2018 ). 2018, 3346--3359. Opgehaal van. https:\/\/aclanthology.org\/C18-1283\/ Thorne, J., Vlachos, A. Automated fact checking: task formulations, methods and future directions. In E.M. Bender, L. Derczynski, P. Isabelle, eds. Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018 (Santa Fe, New Mexico, USA, August 20--26, 2018). 2018, 3346--3359. 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