{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:21:47Z","timestamp":1772302907408,"version":"3.50.1"},"reference-count":41,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2025,3,7]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Fake news refers to intentionally fabricated or misleading information designed to deceive the public and manipulate opinions for personal, political or financial gain. Most existing detection methods primarily focus on capturing language features from news content. However, these methods neglect the varying importance of different news entities. Additionally, these methods tend to overlook the auxiliary role of external knowledge, resulting in an incomplete understanding of the entity. To address these issues, this paper aims to propose a Dual-layer Semantic Information Extraction Network with External Knowledge (DSEN-EK) for fake news detection.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>This approach is proposed to comprise three parts: Dual-layer Semantic Information Extraction Network, Entity Integration Network with External Knowledge and Classifier. Specifically, Dual-layer Semantic Information Extraction Network is designed to enhance relationships between entities and the influence of important entity representations. The Entity Integration Network with External Knowledge is proposed to extract entity descriptions from external knowledge bases.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The DSEN-EK model performs well on the Liar, Constraint, Twitter15 and Twitter16 data sets, achieving accuracy of 98.02%, 94.61%, 90.09% and 93.65%, respectively. These results highlight its effectiveness in detecting fake news across different types of content.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The Dual-layer Semantic Information Extraction Network is proposed to capture the relationships between entities and enhance the continuous semantic information of the news. The Entity Integration Network with External Knowledge is designed to enrich entity descriptions, leading to a more comprehensive capture of semantic details.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-09-2024-0280","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T02:47:12Z","timestamp":1737600432000},"page":"139-157","source":"Crossref","is-referenced-by-count":2,"title":["DSEN-EK: Dual-layer Semantic Information Extraction Network with External Knowledge for Fake News Detection"],"prefix":"10.1108","volume":"21","author":[{"given":"Yanfang","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Run","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenxiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"key2025030706125095900_ref001","doi-asserted-by":"crossref","first-page":"125024","DOI":"10.1016\/j.eswa.2024.125024","article-title":"Enhancing hierarchical attention networks with cnn and stylistic features for fake news detection","volume":"257","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"16","key":"key2025030706125095900_ref002","doi-asserted-by":"crossref","first-page":"18971","DOI":"10.1007\/s10489-023-04455-1","article-title":"Intra-graph and inter-graph joint information propagation network with third-order text graph tensor for fake news detection","volume":"53","year":"2023","journal-title":"Applied Intelligence"},{"key":"key2025030706125095900_ref003","article-title":"Be more with less: hypergraph attention networks for inductive text classification","year":"2020"},{"key":"key2025030706125095900_ref004","first-page":"659","article-title":"Interpretable fake news detection with graph evidence","year":"2023"},{"key":"key2025030706125095900_ref005","first-page":"1","article-title":"Tiefake: title-text similarity and emotion-aware fake news detection","volume-title":"2023 International Joint Conference on Neural Networks (IJCNN)","year":"2023"},{"key":"key2025030706125095900_ref006","first-page":"2931","article-title":"Truth of varying shades: analyzing language in fake news and political fact-checking","year":"2017"},{"key":"key2025030706125095900_ref007","first-page":"754","article-title":"Compare to the knowledge: graph neural fake news detection with external knowledge","year":"2021"},{"key":"key2025030706125095900_ref008","first-page":"102","article-title":"Fake news detection using deep learning","volume-title":"2020 IEEE 10th symposium on computer applications and industrial electronics (ISCAIE)","year":"2020"},{"key":"key2025030706125095900_ref009","article-title":"Pre-training of deep bidirectional transformers for language understanding","year":"2018"},{"key":"key2025030706125095900_ref010","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.1145\/3580305.3599873","article-title":"Muser: a multi-step evidence retrieval enhancement framework for fake news detection","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","year":"2023"},{"key":"key2025030706125095900_ref011","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","article-title":"Bidirectional LSTM with attention mechanism and convolutional layer for text classification","volume":"337","year":"2019","journal-title":"Neurocomputing"},{"key":"key2025030706125095900_ref012","first-page":"1867","article-title":"Real-time rumor debunking on twitter","year":"2015"},{"issue":"2","key":"key2025030706125095900_ref013","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1108\/IJWIS-12-2023-0242","article-title":"Graph-based multi-information integration network with external news environment perception for propaganda detection","volume":"20","year":"2024","journal-title":"International Journal of Web Information Systems"},{"key":"key2025030706125095900_ref014","article-title":"Detecting rumors from microblogs with recurrent neural networks","year":"2016"},{"issue":"7","key":"key2025030706125095900_ref015","doi-asserted-by":"crossref","first-page":"8354","DOI":"10.1007\/s10489-022-03910-9","article-title":"Dc-cnn: dual-channel convolutional neural networks with attention-pooling for fake news detection","volume":"53","year":"2023","journal-title":"Applied Intelligence"},{"issue":"4","key":"key2025030706125095900_ref016","doi-asserted-by":"crossref","first-page":"103354","DOI":"10.1016\/j.ipm.2023.103354","article-title":"Dual emotion based fake news detection: a deep attention-weight update approach","volume":"60","year":"2023","journal-title":"Information Processing and Management"},{"key":"key2025030706125095900_ref017","first-page":"6086","article-title":"A survey on natural language processing for fake news detection","year":"2020"},{"key":"key2025030706125095900_ref018","first-page":"669","article-title":"Content based fake news detection using knowledge graphs","year":"2018"},{"key":"key2025030706125095900_ref019","first-page":"21","article-title":"Fighting an infodemic: covid-19 fake news dataset","volume-title":"Combating Online Hostile Posts in Regional Languages during Emergency Situation: First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, February 8, 2021, Revised Selected Papers 1","year":"2021"},{"key":"key2025030706125095900_ref020","first-page":"25","article-title":"Convolutional neural networks for sentence classification","volume":"6","year":"2016","journal-title":"GitHub"},{"issue":"5\/6","key":"key2025030706125095900_ref021","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1108\/IJWIS-02-2022-0044","article-title":"Fake news detection on twitter","volume":"18","year":"2022","journal-title":"International Journal of Web Information Systems"},{"key":"key2025030706125095900_ref022","article-title":"Zoom out and observe: news environment perception for fake news detection","year":"2022"},{"key":"key2025030706125095900_ref023","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/MIPR.2018.00092","article-title":"Understanding user profiles on social media for fake news detection","volume-title":"2018 IEEE conference on multimedia information processing and retrieval (MIPR)","year":"2018"},{"issue":"1","key":"key2025030706125095900_ref024","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: a data mining perspective","volume":"19","year":"2017","journal-title":"ACM SIGKDD Explorations Newsletter"},{"issue":"3","key":"key2025030706125095900_ref025","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1089\/big.2020.0062","article-title":"Fakenewsnet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media","volume":"8","year":"2020","journal-title":"Big Data"},{"key":"key2025030706125095900_ref026","first-page":"5248","article-title":"Hg-sl: jointly learning of global and local user spreading behavior for fake news early detection","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","year":"2023"},{"issue":"15","key":"key2025030706125095900_ref027","doi-asserted-by":"crossref","first-page":"17652","DOI":"10.1007\/s10489-022-03185-0","article-title":"Long text feature extraction network with data augmentation","volume":"52","year":"2022","journal-title":"Applied Intelligence"},{"issue":"5","key":"key2025030706125095900_ref028","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/72.788640","article-title":"An overview of statistical learning theory","volume":"10","year":"1999","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"6380","key":"key2025030706125095900_ref029","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1126\/science.aap9559","article-title":"The spread of true and false news online","volume":"359","year":"2018","journal-title":"Science"},{"key":"key2025030706125095900_ref030","article-title":"A new benchmark dataset for fake news detection","year":"2021"},{"key":"key2025030706125095900_ref031","article-title":"Liar, liar pants on fire\u201d: a new benchmark dataset for fake news detection","year":"2017"},{"key":"key2025030706125095900_ref032","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/ICPR56361.2022.9956075","article-title":"Induct-gcn: inductive graph convolutional networks for text classification","volume-title":"2022 26th International Conference on Pattern Recognition (ICPR)","year":"2022"},{"key":"key2025030706125095900_ref033","article-title":"Different absorption from the same sharing: sifted multi-task learning for fake news detection","year":"2019"},{"issue":"3","key":"key2025030706125095900_ref034","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3450352","article-title":"Hgat: heterogeneous graph attention networks for semi-supervised short text classification","volume":"39","year":"2021","journal-title":"ACM Transactions on Information Systems"},{"key":"key2025030706125095900_ref035","first-page":"2253","article-title":"Reinforcement subgraph reasoning for fake news detection","year":"2022"},{"issue":"1","key":"key2025030706125095900_ref036","doi-asserted-by":"crossref","first-page":"7370","DOI":"10.1609\/aaai.v33i01.33017370","article-title":"Graph convolutional networks for text classification","volume":"33","year":"2019","journal-title":"In Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"key2025030706125095900_ref037","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1145\/3589334.3645680","article-title":"Heterogeneous subgraph transformer for fake news detection","volume-title":"Proceedings of the ACM on Web Conference 2024","year":"2024"},{"key":"key2025030706125095900_ref038","article-title":"Every document owns its structure: inductive text classification via graph neural networks","year":"2020"},{"issue":"10","key":"key2025030706125095900_ref039","article-title":"Rumor detection with hierarchical representation on bipartite ad hoc event trees","volume":"35","year":"2023","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"key2025030706125095900_ref040","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1007\/978-3-030-75762-5_33","article-title":"Fake news detection with heterogenous deep graph convolutional network[C]","volume":"12712","year":"2021","journal-title":"In Pacific-Asia Conference on Knowledge Discovery and Data Mining"},{"issue":"7","key":"key2025030706125095900_ref041","first-page":"7178","article-title":"Memory-guided multi-view multi-domain fake news detection","volume":"35","year":"2022","journal-title":"IEEE Transactions on Knowledge and Data Engineering"}],"container-title":["International Journal of Web Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-09-2024-0280\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJWIS-09-2024-0280\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:24:18Z","timestamp":1753395858000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijwis\/article\/21\/2\/139-157\/1239956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":41,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,1,24]]},"published-print":{"date-parts":[[2025,3,7]]}},"alternative-id":["10.1108\/IJWIS-09-2024-0280"],"URL":"https:\/\/doi.org\/10.1108\/ijwis-09-2024-0280","relation":{},"ISSN":["1744-0084","1744-0092"],"issn-type":[{"value":"1744-0084","type":"print"},{"value":"1744-0092","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]}}}