{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T17:25:18Z","timestamp":1763227518769,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819983872"},{"type":"electronic","value":"9789819983889"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8388-9_41","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T16:02:21Z","timestamp":1701014541000},"page":"507-519","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["COVID-19 Fake News Detection Using Cross-Domain Classification Techniques"],"prefix":"10.1007","author":[{"given":"Arnav","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Subhanjali","family":"Sharma","sequence":"additional","affiliation":[]},{"given":"Utkarsh","family":"Bhardwaj","sequence":"additional","affiliation":[]},{"given":"Sajib","family":"Mistry","sequence":"additional","affiliation":[]},{"given":"Novarun","family":"Deb","sequence":"additional","affiliation":[]},{"given":"Aneesh","family":"Krishna","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"41_CR1","unstructured":"Fake News Detection Datasets - University of Victoria. https:\/\/www.uvic.ca\/ecs\/ece\/isot\/datasets\/fake-news\/index.php"},{"key":"41_CR2","unstructured":"Fake News. https:\/\/kaggle.com\/competitions\/fake-news"},{"key":"41_CR3","unstructured":"Wang, S.: File structure (2022). https:\/\/github.com\/MickeysClubhouse\/COVID-19-rumor-dataset. Accessed 24 May 2022"},{"key":"41_CR4","unstructured":"Cui, L., Lee, D.: CoAID: COVID-19 healthcare misinformation dataset. arXiv (2020). http:\/\/arxiv.org\/abs\/2006.00885. Accessed 24 May 2022"},{"key":"41_CR5","doi-asserted-by":"publisher","unstructured":"Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1532\u20131543 (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"41_CR6","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv (2013). http:\/\/arxiv.org\/abs\/1301.3781. Accessed 30 May 2022"},{"issue":"1","key":"41_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/pra2.2015.145052010082","volume":"52","author":"NJ Conroy","year":"2015","unstructured":"Conroy, N.J., Rubin, V.L., Chen, Y.: Automatic deception detection: methods for finding fake news. Proc. Assoc. Inf. Sci. Technol. 52(1), 1\u20134 (2015)","journal-title":"Proc. Assoc. Inf. Sci. Technol."},{"key":"41_CR8","unstructured":"Khurana, U., Intelligentie, B.O.K.: The linguistic features of fake news headlines and statements (2017)"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Bhattacharjee, S.D., Talukder, A., Balantrapu, B.V.: Active learning based news veracity detection with feature weighting and deep-shallow fusion. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 556\u2013565. IEEE (2017)","DOI":"10.1109\/BigData.2017.8257971"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Hassan, N., Arslan, F., Li, C., Tremayne, M.: Toward automated fact-checking: detecting check-worthy factual claims by ClaimBuster. In: SIGKDD, pp. 1803\u20131812 (2017)","DOI":"10.1145\/3097983.3098131"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Rashkin, H., Choi, E., Jang, J.Y., Volkova, S., Choi, Y.: Truth of varying shades: analyzing language in fake news and political fact-checking. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2931\u20132937 (2017)","DOI":"10.18653\/v1\/D17-1317"},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Wang, W.Y.: \u201cLiar, liar pants on fire\u201d: a new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648 (2017)","DOI":"10.18653\/v1\/P17-2067"},{"issue":"8","key":"41_CR13","doi-asserted-by":"publisher","first-page":"11765","DOI":"10.1007\/s11042-020-10183-2","volume":"80","author":"RK Kaliyar","year":"2021","unstructured":"Kaliyar, R.K., Goswami, A., Narang, P.: FakeBERT: fake news detection in social media with a BERT-based deep learning approach. Multimed. Tools Appl. 80(8), 11765\u201311788 (2021). https:\/\/doi.org\/10.1007\/s11042-020-10183-2","journal-title":"Multimed. Tools Appl."},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Ramponi, A., Plank, B.: Neural unsupervised domain adaptation in NLP\u2013a survey. arXiv (2020)","DOI":"10.18653\/v1\/2020.coling-main.603"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, B., Zhang, X., Liu, Y., Cheng, L., Li, Z.: Matching distributions between model and data: cross-domain knowledge distillation for unsupervised domain adaptation. In: ICONIP, pp. 5423\u20135433 (2021)","DOI":"10.18653\/v1\/2021.acl-long.421"},{"key":"41_CR16","doi-asserted-by":"crossref","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. In: HLT, pp. 2227\u20132237 (2018)","DOI":"10.18653\/v1\/N18-1202"},{"key":"41_CR17","unstructured":"Kouw, W.M., Loog, M.: An introduction to domain adaptation and transfer learning. arXiv (2019). http:\/\/arxiv.org\/abs\/1812.11806. Accessed 24 May 2022"},{"key":"41_CR18","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. arXiv (2016). http:\/\/arxiv.org\/abs\/1505.07818. Accessed 25 May 2022"},{"key":"41_CR19","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. arXiv (2015). http:\/\/arxiv.org\/abs\/1409.7495. Accessed 25 May 2022"},{"issue":"6","key":"41_CR20","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129(6), 1789\u20131819 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01453-z","journal-title":"Int. J. Comput. Vision"},{"key":"41_CR21","unstructured":"M\u00fcller, M., Salath\u00e9, M., Kummervold, P.E.: COVID-twitter-BERT: a natural language processing model to analyse COVID-19 content on twitter. arXiv (2020)"},{"key":"41_CR22","unstructured":"Multi-Domain Sentiment Dataset. https:\/\/www.cs.jhu.edu\/~mdredze\/datasets\/sentiment\/"}],"container-title":["Lecture Notes in Computer Science","AI 2023: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8388-9_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:57:10Z","timestamp":1710356230000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8388-9_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819983872","9789819983889"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8388-9_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ajcai2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"213","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}