{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T21:24:43Z","timestamp":1777325083204,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T00:00:00Z","timestamp":1630195200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T00:00:00Z","timestamp":1630195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06450-4","type":"journal-article","created":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T06:02:31Z","timestamp":1630216951000},"page":"771-782","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["AENeT: an attention-enabled neural architecture for fake news detection using contextual features"],"prefix":"10.1007","volume":"34","author":[{"given":"Vidit","family":"Jain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rohit Kumar","family":"Kaliyar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anurag","family":"Goswami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pratik","family":"Narang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yashvardhan","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,29]]},"reference":[{"key":"6450_CR1","unstructured":"Srijan K, Neil S (2018) False information on web and social media: a survey"},{"issue":"1","key":"6450_CR2","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newslett 19(1):22\u201336","journal-title":"ACM SIGKDD Explor Newslett"},{"issue":"2","key":"6450_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1353\/jod.2017.0025","volume":"28","author":"N Persily","year":"2017","unstructured":"Persily N (2017) The 2016 US election: Can democracy survive the internet?. J Democracy 28(2):63\u201376","journal-title":"J Democracy"},{"key":"6450_CR4","doi-asserted-by":"crossref","unstructured":"Wang WY (2017) Liar, Liar Pants on Fire\u201d: a new benchmark dataset for fake news detection. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 2: Short Papers), pp. 422-426","DOI":"10.18653\/v1\/P17-2067"},{"key":"6450_CR5","doi-asserted-by":"crossref","unstructured":"Conroy NJ, Rubin VL, and Chen Y (2015) Automatic deception detection: methods for finding fake news. In: Proceedings of the 78th ASIS&T annual meeting: information science with impact: research in and for the community, pp. 1-4","DOI":"10.1002\/pra2.2015.145052010082"},{"key":"6450_CR6","unstructured":"Long, Yunfei, Qin Lu, Rong Xiang, Minglei Li, and Chu-Ren Huang. \u201cFake News Detection Through Multi-Perspective Speaker Profiles.\u201d In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 252-256. 2017"},{"key":"6450_CR7","doi-asserted-by":"crossref","unstructured":"Dougherty J, Ron K, and Mehran S (1995) Supervised and unsupervised discretization of continuous features. In: Machine learning proceedings 1995, pp. 194-202. Morgan Kaufmann","DOI":"10.1016\/B978-1-55860-377-6.50032-3"},{"key":"6450_CR8","unstructured":"Mikolov T, Kai C, Greg C and Jeffrey D (2013) Efficient Estimation of Word Representations in Vector Space"},{"key":"6450_CR9","doi-asserted-by":"crossref","unstructured":"Ahmed H, Issa T, and Sherif S (2017) Detection of online fake news using N-gram analysis and machine learning techniques. In: International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, pp. 127-138. Springer, Cham,","DOI":"10.1007\/978-3-319-69155-8_9"},{"key":"6450_CR10","unstructured":"Vasudevan V, Barret Z, Jonathon S, and Le QV (2019) Neural architecture search for convolutional neural networks. U.S. Patent 10,521,729, issued December 31"},{"key":"6450_CR11","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, and Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532-1543","DOI":"10.3115\/v1\/D14-1162"},{"key":"6450_CR12","unstructured":"Zhang Y, Wallace BC (2017) A sensitivity analysis of (and Practitioners\u2019 Guide to) Convolutional Neural Networks for Sentence Classification. In: Proceedings of the eighth international joint conference on natural language processing (Volume 1: Long Papers), pp. 253-263"},{"issue":"6334","key":"6450_CR13","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1126\/science.aal4230","volume":"356","author":"Aylin Caliskan","year":"2017","unstructured":"Caliskan Aylin, Bryson Joanna J, Narayanan Arvind (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183\u2013186","journal-title":"Science"},{"key":"6450_CR14","doi-asserted-by":"crossref","unstructured":"Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014) Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp. 1555-1565","DOI":"10.3115\/v1\/P14-1146"},{"key":"6450_CR15","doi-asserted-by":"crossref","unstructured":"Zhong Botao, Xing Xuejiao, Peter Love Xu, Wang, and Hanbin Luo. (2019) Convolutional neural network: Deep learning-based classification of building quality problems. Adv Eng Inform 40:46\u201357","DOI":"10.1016\/j.aei.2019.02.009"},{"key":"6450_CR16","doi-asserted-by":"crossref","unstructured":"Zhang T, Wang D, Chen H, Zeng Z, Guo W, Miao C, Cui L (2020) BDANN: BERT-based domain adaptation neural network for multi-modal fake news detection. In: 2020 international joint conference on neural networks (IJCNN), pp. 1-8. IEEE","DOI":"10.1109\/IJCNN48605.2020.9206973"},{"key":"6450_CR17","doi-asserted-by":"crossref","unstructured":"Shu K, Wang S, Liu H (2019) Beyond news contents: the role of social context for fake news detection. In: Proceedings of the Twelfth ACM international conference on web search and data mining, pp. 312-320. ACM","DOI":"10.1145\/3289600.3290994"},{"key":"6450_CR18","doi-asserted-by":"crossref","unstructured":"Peters M, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, and Zettlemoyer L (2018) Dep contextualized word representations. In: Proceedings of the 2018 conference of the north american chapter of the association for computational linguistics: human language technologies, Volume 1 (Long Papers), pp. 2227-2237","DOI":"10.18653\/v1\/N18-1202"},{"key":"6450_CR19","unstructured":"Roy A, Basak K, Ekbal A,  Bhattacharyya P (2018)  A deep ensemble framework for fake news detection and classification"},{"key":"6450_CR20","doi-asserted-by":"crossref","unstructured":"Wang Y, Ma F, Jin Z, Yuan Y, Xun G,  Jha K, Su L, Gao J (2018) Eann: event adversarial neural networks for multi-modal fake news detection. In: Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining, pp. 849-857","DOI":"10.1145\/3219819.3219903"},{"key":"6450_CR21","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.artint.2016.07.005","volume":"240","author":"J Camacho-Collados","year":"2017","unstructured":"Camacho-Collados J, Pilehvar MT, and Navigli R  (2016) Nasari: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities. Artif Intell 240:36-64","journal-title":"Artif Intell"},{"key":"6450_CR22","doi-asserted-by":"crossref","unstructured":"Iyyer M,  Manjunatha V, Boyd-Graber J, and Hal Daum\u00e9 III (2015) Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers), pp. 1681-1691","DOI":"10.3115\/v1\/P15-1162"},{"key":"6450_CR23","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.eswa.2017.08.021","volume":"90","author":"M Kamkarhaghighi","year":"2017","unstructured":"Kamkarhaghighi M, Makrehchi M (2017) Content tree word embedding for document representation. Expert Syst Appl 90:241\u2013249","journal-title":"Expert Systems with Applications"},{"key":"6450_CR24","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.csl.2017.07.009","volume":"47","author":"C Cerisara","year":"2018","unstructured":"Cerisara C, Kral P, Lenc L (2018) On the effects of using word2vec representations in neural networks for dialogue act recognition. Comput Speech  Language 47:175\u2013193","journal-title":"Comput Speech Language"},{"key":"6450_CR25","unstructured":"Vaswani  A, Noam S, Niki P, Jakob U, Llion J , Gomez AN, Lukasz K, and Illia P (2017) Attention is All you Need. InL NIPS"},{"issue":"3","key":"6450_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3305260","volume":"10","author":"K Sharma","year":"2019","unstructured":"Sharma K, Qian F, Jiang H, Ruchansky N, Zhang M, Liu Y (2019) Combating fake news: a survey on identification and mitigation techniques. ACM Trans Intell Syst Technol (TIST) 10(3):1\u201342","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"6450_CR27","doi-asserted-by":"crossref","unstructured":"Ruchansky N, Sungyong S, and Yan L (2017) Csi: a hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp. 797-806. ACM","DOI":"10.1145\/3132847.3132877"},{"issue":"3","key":"6450_CR28","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1089\/big.2020.0062","volume":"8","author":"K Shu","year":"2020","unstructured":"Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2020) FakeNewsNet: a data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8(3):171\u2013188","journal-title":"Big Data"},{"key":"6450_CR29","unstructured":"Feng S, Ritwik B, and Yejin C (2012) Syntactic stylometry for deception detection. In: Proceedings of the 50th annual meeting of the association for computational linguistics: Short Papers-Volume 2, pp. 171-175. Association for Computational Linguistics"},{"key":"6450_CR30","unstructured":"P\u00c9rez-Rosas V, Bennett K, Alexandra L, and Rada M (2018) Automatic Detection of Fake News. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 3391-3401"},{"key":"6450_CR31","doi-asserted-by":"crossref","unstructured":"Shu K, Limeng C, Suhang W, Dongwon L, and Huan L (2019) Defend: explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 395-405","DOI":"10.1145\/3292500.3330935"},{"key":"6450_CR32","doi-asserted-by":"crossref","unstructured":"Zhang J, Bowen D, and  Yu Philip S (2020) Fakedetector: effective fake news detection with deep diffusive neural network. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 1826-1829. IEEE","DOI":"10.1109\/ICDE48307.2020.00180"},{"key":"6450_CR33","doi-asserted-by":"crossref","unstructured":"Rohit KK , Anurag Gi, Pratik N, and Soumendu S (2020) FNDNet\u2013a deep convolutional neural network for fake news detection. Cognit Syst Res 61: 32\u201344","DOI":"10.1016\/j.cogsys.2019.12.005"},{"key":"6450_CR34","doi-asserted-by":"crossref","unstructured":"Kaliyar RK, Anurag G, and Pratik N (2021) EchoFakeD: improving fake news detection in social media with an efficient deep neural network. Neural Computing and Applications 1-17","DOI":"10.1007\/s00521-020-05611-1"},{"key":"6450_CR35","doi-asserted-by":"crossref","unstructured":"Kaliyar RK, Anurag G, and Pratik N (2021) MCNNet: generalizing Fake News Detection with a Multichannel Convolutional Neural Network using a Novel COVID-19 Dataset. In: 8th ACM IKDD CODS and 26th COMAD, pp. 437-437","DOI":"10.1145\/3430984.3431064"},{"key":"6450_CR36","doi-asserted-by":"crossref","unstructured":"Pennington J, Richard S, and Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532-1543","DOI":"10.3115\/v1\/D14-1162"},{"key":"6450_CR37","doi-asserted-by":"crossref","unstructured":"Ren Y, Bo W, Jiawei Z, and Yi C (2020) Adversarial active learning based heterogeneous graph neural network for fake news detection. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 452-461. IEEE,","DOI":"10.1109\/ICDM50108.2020.00054"},{"key":"6450_CR38","doi-asserted-by":"crossref","unstructured":"Wang Y, Shengsheng Q, Jun H, Quan F, and Changsheng X (2020) Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp. 540-547","DOI":"10.1145\/3372278.3390713"},{"key":"6450_CR39","doi-asserted-by":"publisher","first-page":"106561","DOI":"10.1016\/j.knosys.2020.106561","volume":"211","author":"C Qi","year":"2021","unstructured":"Qi C, Zhang J, Jia H, Mao Q, Wang L, Song H (2021) Deep face clustering using residual graph convolutional network. Knowl Based Syst 211:106561","journal-title":"Knowl Based Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06450-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06450-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06450-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T09:44:12Z","timestamp":1744191852000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06450-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,29]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6450"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06450-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,29]]},"assertion":[{"value":"5 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors of this manuscript certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers\u2019 bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}