{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T02:18:16Z","timestamp":1781144296487,"version":"3.54.1"},"reference-count":74,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:00:00Z","timestamp":1696982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["1943370"],"award-info":[{"award-number":["1943370"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>The presence of fake news on online social media is overwhelming and is responsible for having impacted several aspects of people\u2019s lives, from health to politics, the economy, and response to natural disasters. Although significant effort has been made to mitigate fake news spread, current research focuses on single aspects of the problem, such as detecting fake news spreaders and classifying stories as either factual or fake. In this article, we propose a new method to exploit inter-relationships between stories, sources, and final users and integrate prior knowledge of these three entities to jointly estimate the credibility degree of each entity involved in the news ecosystem. Specifically, we develop a new graph convolutional network, namely, Role-Relational Graph Convolutional Networks (Role-RGCN), to learn, for each node type (or role), a unique node representation space and jointly connect the different representation spaces with edge relations. To test our proposed approach, we conducted an experimental evaluation on the state-of-the-art FakeNewsNet-Politifact dataset and a new dataset with ground truth on news credibility degrees we collected. Experimental results show a superior performance of our Role-RGCN proposed method at predicting the credibility degree of stories, sources, and users compared to state-of-the-art approaches and other baselines.<\/jats:p>","DOI":"10.1145\/3617418","type":"journal-article","created":{"date-parts":[[2023,8,26]],"date-time":"2023-08-26T10:38:05Z","timestamp":1693046285000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Joint Credibility Estimation of News, User, and Publisher via Role-relational Graph Convolutional Networks"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5379-695X","authenticated-orcid":false,"given":"Anu","family":"Shrestha","sequence":"first","affiliation":[{"name":"Boise State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7976-4203","authenticated-orcid":false,"given":"Jason","family":"Duran","sequence":"additional","affiliation":[{"name":"Boise State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-9728","authenticated-orcid":false,"given":"Francesca","family":"Spezzano","sequence":"additional","affiliation":[{"name":"Boise State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0689-5063","authenticated-orcid":false,"given":"Edoardo","family":"Serra","sequence":"additional","affiliation":[{"name":"Boise State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"key":"e_1_3_4_2_2","article-title":"News on Twitter: Consumed by most users and trusted by many","author":"Mitchell Elisa Shearer, Amy","year":"2021","unstructured":"Elisa Shearer, Amy Mitchell, and Galen Stocking. 2021. News on Twitter: Consumed by most users and trusted by many. Pew Research Center (2021). https:\/\/www.pewresearch.org\/journalism\/2021\/11\/15\/news-on-twitter-consumed-by-most-users-and-trusted-by-many\/","journal-title":"Pew Research Center"},{"key":"e_1_3_4_3_2","first-page":"3391","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics","author":"P\u00e9rez-Rosas Ver\u00f3nica","year":"2018","unstructured":"Ver\u00f3nica P\u00e9rez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2018. Automatic detection of fake news. In Proceedings of the 27th International Conference on Computational Linguistics. 3391\u20133401."},{"key":"e_1_3_4_4_2","article-title":"This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news","author":"Horne Benjamin D.","year":"2017","unstructured":"Benjamin D. Horne and Sibel Adali. 2017. This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In Proceedings of the 2nd International Workshop on News and Public Opinion at ICWSM.","journal-title":"Proceedings of the 2nd International Workshop on News and Public Opinion at ICWSM"},{"key":"e_1_3_4_5_2","first-page":"231","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","author":"Potthast Martin","year":"2018","unstructured":"Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2018. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 231\u2013240."},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963500"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10382"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1174"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304302"},{"key":"e_1_3_4_11_2","first-page":"797","volume-title":"Proceedings of the Conference on Information and Knowledge Management","author":"Ruchansky Natali","year":"2017","unstructured":"Natali Ruchansky, Sungyong Seo, and Yan Liu. 2017. CSI: A hybrid deep model for fake news detection. In Proceedings of the Conference on Information and Knowledge Management, Ee-Peng Lim, Marianne Winslett, Mark Sanderson, Ada Wai-Chee Fu, Jimeng Sun, J. Shane Culpepper, Eric Lo, Joyce C. Ho, Debora Donato, Rakesh Agrawal, Yu Zheng, Carlos Castillo, Aixin Sun, Vincent S. Tseng, and Chenliang Li (Eds.). ACM, 797\u2013806."},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271709"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330935"},{"key":"e_1_3_4_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462990"},{"key":"e_1_3_4_15_2","unstructured":"Francisco M. Rangel Pardo Anastasia Giachanou Bilal Ghanem and Paolo Rosso. 2020. Overview of the 8th author profiling task at {PAN} 2020: Profiling fake news spreaders on twitter. Working Notes of CLEF\u201920-Conference and Labs of the Evaluation Forum Thessaloniki Greece September 22-25 2020 (CEUR Workshop Proceedings) Linda Cappellato Carsten Eickhoff Nicola Ferro and Aur\u00e9lie N\u00e9v\u00e9ol (Eds.). Vol. 2696 CEUR-WS.org. https:\/\/ceur-ws.org\/Vol-2696\/paper_267.pdf"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-021-00291-z"},{"key":"e_1_3_4_17_2","first-page":"43","volume-title":"Studying Fake News via Network Analysis: Detection and Mitigation","author":"Shu Kai","year":"2019","unstructured":"Kai Shu, H. Russell Bernard, and Huan Liu. 2019. Studying Fake News via Network Analysis: Detection and Mitigation. Springer International Publishing, Cham, 43\u201365."},{"key":"e_1_3_4_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3305260"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2010.12.007"},{"key":"e_1_3_4_20_2","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1145\/2487788.2488033","volume-title":"Proceedings of the 22nd International Conference on World Wide Web","author":"Gupta Aditi","year":"2013","unstructured":"Aditi Gupta, Hemank Lamba, Ponnurangam Kumaraguru, and Anupam Joshi. 2013. Faking Sandy: Characterizing and identifying fake images on Twitter during Hurricane Sandy. In Proceedings of the 22nd International Conference on World Wide Web. 729\u2013736."},{"key":"e_1_3_4_21_2","article-title":"Rumors, false flags, and digital vigilantes: Misinformation on Twitter after the 2013 Boston Marathon bombing","author":"Starbird Kate","year":"2014","unstructured":"Kate Starbird, Jim Maddock, Mania Orand, Peg Achterman, and Robert M. Mason. 2014. Rumors, false flags, and digital vigilantes: Misinformation on Twitter after the 2013 Boston Marathon bombing. IConference 2014 Proceedings (2014).","journal-title":"IConference 2014 Proceedings"},{"issue":"3","key":"e_1_3_4_22_2","article-title":"Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on Twitter","volume":"12","author":"Kouzy Ramez","year":"2020","unstructured":"Ramez Kouzy, Joseph Abi Jaoude, Afif Kraitem, Molly B. El Alam, Basil Karam, Elio Adib, Jabra Zarka, Cindy Traboulsi, Elie W. Akl, and Khalil Baddour. 2020. Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on Twitter. Cureus 12, 3 (2020).","journal-title":"Cureus"},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2022.100531"},{"key":"e_1_3_4_24_2","first-page":"312","volume-title":"Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM\u201919)","author":"Shu Kai","year":"2019","unstructured":"Kai Shu, Suhang Wang, and Huan Liu. 2019. Beyond news contents: The role of social context for fake news detection. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (WSDM\u201919). Association for Computing Machinery, New York, NY, 312\u2013320."},{"key":"e_1_3_4_25_2","volume-title":"Proceedings of the 1st IEEE International Workshop on Fake MultiMedia (FakeMM\u201918)","author":"Shu Kai","year":"2018","unstructured":"Kai Shu, Suhang Wang, and Huan Liu. 2018. Understanding user profiles on social media for fake news detection. In Proceedings of the 1st IEEE International Workshop on Fake MultiMedia (FakeMM\u201918)."},{"key":"e_1_3_4_26_2","first-page":"623","volume-title":"Handbook of Media Economics","author":"Gentzkow Matthew","year":"2015","unstructured":"Matthew Gentzkow, Jesse M. Shapiro, and Daniel F. Stone. 2015. Media bias in the marketplace: Theory. In Handbook of Media Economics, Vol. 1. Elsevier, 623\u2013645."},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1460-2466.2006.00336.x"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13721-020-0226-0"},{"issue":"1","key":"e_1_3_4_29_2","article-title":"That\u2019s fake news! Investigating how readers identify the reliability of news when provided title, image, source bias, and full articles","volume":"5","author":"Spezzano Francesca","year":"2021","unstructured":"Francesca Spezzano, Anu Shrestha, Jerry Alan Fails, and Brian W. Stone. 2021. That\u2019s fake news! Investigating how readers identify the reliability of news when provided title, image, source bias, and full articles. Proc. ACM Hum. Comput. Interact. J. 5, CSCW1, Article 109 (2021).","journal-title":"Proc. ACM Hum. Comput. Interact. J."},{"key":"e_1_3_4_30_2","doi-asserted-by":"crossref","unstructured":"Shelby L. Bandel Allison E. Bond Craig J. Bryan and Michael D. Anestis. 2023. Public perception of gun violence-related headline accuracy and the credibility of media sources. Health Communication 38 9 (2023) 1856\u20131861.","DOI":"10.1080\/10410236.2022.2037199"},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_4_32_2","article-title":"FakeNewsNet: A data repository with news content, social context and dynamic information for studying fake news on social media","volume":"8","author":"Shu Kai","year":"2018","unstructured":"Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2018. FakeNewsNet: A data repository with news content, social context and dynamic information for studying fake news on social media. arXiv preprint arXiv:1809.01286 8 (2018).","journal-title":"arXiv preprint arXiv:1809.01286"},{"key":"e_1_3_4_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3305260"},{"key":"e_1_3_4_34_2","first-page":"289","volume-title":"Proceedings of the ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW\u201918)","author":"Che Xunru","year":"2018","unstructured":"Xunru Che, D. Metaxa-Kakavouli, and Jeffrey T. Hancock. 2018. Fake news in the news: An analysis of partisan coverage of the fake news phenomenon. In Proceedings of the ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW\u201918). Association for Computing Machinery, 289\u2013292."},{"key":"e_1_3_4_35_2","unstructured":"Yunfei Long Qin Lu Rong Xiang Minglei Li and Chu-Ren Huang. 2017. Fake news detection through multi-perspective speaker profiles. In Proceedings of the 8th International Joint Conference on Natural Language Processing (volume 2: Short papers) 252\u2013256."},{"key":"e_1_3_4_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94105-9_3"},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103354"},{"key":"e_1_3_4_38_2","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1007\/978-3-030-47436-2_27","volume-title":"Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining","author":"Zhou Xinyi","year":"2020","unstructured":"Xinyi Zhou, Jindi Wu, and Reza Zafarani. 2020. SAFE: Similarity-aware multi-modal fake news detection. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 354\u2013367."},{"key":"e_1_3_4_39_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aap9559"},{"key":"e_1_3_4_40_2","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aau4586"},{"key":"e_1_3_4_41_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551520985486"},{"key":"e_1_3_4_42_2","volume-title":"Proceedings of the Conference and Labs of the Evaluation Forum","author":"Buda Jakab","year":"2020","unstructured":"Jakab Buda and Flora Bolonyai. 2020. An ensemble model using n-grams and statistical features to identify fake news spreaders on Twitter. In Proceedings of the Conference and Labs of the Evaluation Forum."},{"key":"e_1_3_4_43_2","volume-title":"Proceedings of the Conference and Labs of the Evaluation Forum","author":"Pizarro Juan","year":"2020","unstructured":"Juan Pizarro. 2020. Using n-grams to detect fake news spreaders on Twitter. In Proceedings of the Conference and Labs of the Evaluation Forum."},{"key":"e_1_3_4_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512122"},{"key":"e_1_3_4_45_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i5.20517"},{"key":"e_1_3_4_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539277"},{"key":"e_1_3_4_47_2","article-title":"Fake news detection on social media using geometric deep learning","author":"Monti Federico","year":"2019","unstructured":"Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, and Michael M. Bronstein. 2019. Fake news detection on social media using geometric deep learning. arXiv preprint arXiv:1902.06673 (2019).","journal-title":"arXiv preprint arXiv:1902.06673"},{"key":"e_1_3_4_48_2","article-title":"Graph neural networks with continual learning for fake news detection from social media","volume":"2007","author":"Han Yi","year":"2020","unstructured":"Yi Han, Shanika Karunasekera, and Christopher Leckie. 2020. Graph neural networks with continual learning for fake news detection from social media. CoRR abs\/2007.03316 (2020).","journal-title":"CoRR"},{"key":"e_1_3_4_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380180"},{"key":"e_1_3_4_50_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.476"},{"key":"e_1_3_4_51_2","first-page":"8783","volume-title":"Proceedings of the 34th AAAI Conference on Artificial Intelligence, the 32nd Innovative Applications of Artificial Intelligence Conference, the 10th AAAI Symposium on Educational Advances in Artificial Intelligence","author":"Khoo Ling Min Serena","year":"2020","unstructured":"Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, and Jing Jiang. 2020. Interpretable rumor detection in microblogs by attending to user interactions. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, the 32nd Innovative Applications of Artificial Intelligence Conference, the 10th AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI Press, 8783\u20138790. Retrieved from https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/6405"},{"key":"e_1_3_4_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00054"},{"key":"e_1_3_4_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412046"},{"key":"e_1_3_4_54_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/545"},{"key":"e_1_3_4_55_2","first-page":"1546","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics","author":"Karimi Hamid","year":"2018","unstructured":"Hamid Karimi, Proteek Roy, Sari Saba-Sadiya, and Jiliang Tang. 2018. Multi-source multi-class fake news detection. In Proceedings of the 27th International Conference on Computational Linguistics, Emily M. Bender, Leon Derczynski, and Pierre Isabelle (Eds.). Association for Computational Linguistics, 1546\u20131557."},{"issue":"2","key":"e_1_3_4_56_2","article-title":"An emotional analysis of false information in social media and news articles","volume":"20","author":"Ghanem Bilal","year":"2020","unstructured":"Bilal Ghanem, Paolo Rosso, and Francisco M. Rangel Pardo. 2020. An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. 20, 2 (2020), 19:1\u201319:18.","journal-title":"ACM Trans. Internet Technol."},{"key":"e_1_3_4_57_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.24480"},{"key":"e_1_3_4_58_2","first-page":"181","volume-title":"Proceedings of the 25th International Conference on Applications of Natural Language to Information Systems","author":"Giachanou Anastasia","unstructured":"Anastasia Giachanou, Esteban A. Rissola, Bilal Ghanem, Fabio Crestani, and Paolo Rosso. 2020. The role of personality and linguistic patterns in discriminating between fake news spreaders and fact checkers. In Proceedings of the 25th International Conference on Applications of Natural Language to Information Systems. Springer Nature, 181."},{"key":"e_1_3_4_59_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2021.101960"},{"key":"e_1_3_4_60_2","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"e_1_3_4_61_2","series-title":"Proceedings of the 43rd European Conference on IR Research: Advances in Information Retrieval","first-page":"120","volume":"12657","author":"Shrestha Anu","year":"2021","unstructured":"Anu Shrestha and Francesca Spezzano. 2021. Textual characteristics of news title and body to detect fake news: A reproducibility study. In Proceedings of the 43rd European Conference on IR Research: Advances in Information Retrieval(Lecture Notes in Computer Science, Vol. 12657). Springer, 120\u2013133."},{"key":"e_1_3_4_62_2","series-title":"Proceedings of the 2nd Multidisciplinary International Symposium on Disinformation in Open Online Media","first-page":"261","volume":"12259","author":"Shrestha Anu","year":"2020","unstructured":"Anu Shrestha, Francesca Spezzano, and Indhumathi Gurunathan. 2020. Multi-modal analysis of misleading political news. In Proceedings of the 2nd Multidisciplinary International Symposium on Disinformation in Open Online Media(Lecture Notes in Computer Science, Vol. 12259). Springer, 261\u2013276."},{"key":"e_1_3_4_63_2","unstructured":"MediaBias\/FactCheck Apps\/Extensions. ([n. d.]). Retrieved from https:\/\/mediabiasfactcheck.com\/"},{"issue":"1","key":"e_1_3_4_64_2","first-page":"1","article-title":"A calibrated measure to compare fluctuations of different entities across timescales","volume":"10","author":"Cho\u0142oniewski Jan","year":"2020","unstructured":"Jan Cho\u0142oniewski, Julian Sienkiewicz, Naum Dretnik, Gregor Leban, Mike Thelwall, and Janusz A. Ho\u0142yst. 2020. A calibrated measure to compare fluctuations of different entities across timescales. Scient. Rep. 10, 1 (2020), 1\u201316.","journal-title":"Scient. Rep."},{"key":"e_1_3_4_65_2","doi-asserted-by":"crossref","unstructured":"Antonela Tommasel and Filippo Menczer. 2022. Do recommender systems make social media more susceptible to misinformation spreaders? InProceedings of the ACM Conference on Recommender Systems (RecSys\u201922). Association for Computing Machinery New York NY.","DOI":"10.1145\/3523227.3551473"},{"key":"e_1_3_4_66_2","doi-asserted-by":"crossref","unstructured":"Karishma Sharma Feng Qian He Jiang Natali Ruchansky Ming Zhang and Yan Liu. 2019. Combating fake news: A survey on identification and mitigation techniques. ACM Transactions on Intelligent Systems and Technology (TIST) 10 3 Article 21 (Apr.2019) 1\u201342. 21","DOI":"10.1145\/3305260"},{"key":"e_1_3_4_67_2","doi-asserted-by":"crossref","unstructured":"Kellin Pelrine Jacob Danovitch and Reihaneh Rabbany. 2021. The surprising performance of simple baselines for misinformation detection. InProceedings of the Web Conference (WWW\u201921). Association for Computing Machinery New York NY.","DOI":"10.1145\/3442381.3450111"},{"key":"e_1_3_4_68_2","unstructured":"Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai Tianjun Xiao Tong He George Karypis Jinyang Li and Zheng Zhang. 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. (2019)."},{"key":"e_1_3_4_69_2","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","volume":"1907","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019).","journal-title":"CoRR"},{"key":"e_1_3_4_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783370"},{"key":"e_1_3_4_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450111"},{"key":"e_1_3_4_72_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.48"},{"issue":"1","key":"e_1_3_4_73_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/0022250X.1980.9989895","article-title":"On the relation between Markov random fields and social networks","volume":"7","author":"Kindermann Ross P.","year":"1980","unstructured":"Ross P. Kindermann and J. Laurie Snell. 1980. On the relation between Markov random fields and social networks. J. Math. Sociol. 7, 1 (1980), 1\u201313.","journal-title":"J. Math. Sociol."},{"issue":"236","key":"e_1_3_4_74_2","first-page":"0018","article-title":"Understanding belief propagation and its generalizations","volume":"8","author":"Yedidia Jonathan S.","year":"2003","unstructured":"Jonathan S. Yedidia, William T. Freeman, Yair Weiss. 2003. Understanding belief propagation and its generalizations. Explor. Artif. Intell. New Millenn. 8, 236-239 (2003), 0018\u20139448.","journal-title":"Explor. Artif. Intell. New Millenn."},{"key":"e_1_3_4_75_2","series-title":"Proceedings of the 43rd European Conference on IR Research: Advances in Information Retrieval","first-page":"120","volume":"12657","author":"Shrestha Anu","year":"2021","unstructured":"Anu Shrestha and Francesca Spezzano. 2021. Textual characteristics of news title and body to detect fake news: A reproducibility study. In Proceedings of the 43rd European Conference on IR Research: Advances in Information Retrieval(Lecture Notes in Computer Science, Vol. 12657). Springer, 120\u2013133."}],"container-title":["ACM Transactions on the Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3617418","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3617418","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:45:54Z","timestamp":1750178754000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3617418"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,11]]},"references-count":74,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2,29]]}},"alternative-id":["10.1145\/3617418"],"URL":"https:\/\/doi.org\/10.1145\/3617418","relation":{},"ISSN":["1559-1131","1559-114X"],"issn-type":[{"value":"1559-1131","type":"print"},{"value":"1559-114X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,11]]},"assertion":[{"value":"2022-09-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-31","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}