{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T01:35:17Z","timestamp":1777599317553,"version":"3.51.4"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031282379","type":"print"},{"value":"9783031282386","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-28238-6_33","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T17:03:18Z","timestamp":1678986198000},"page":"430-438","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Detecting Stance of\u00a0Authorities Towards Rumors in\u00a0Arabic Tweets: A Preliminary Study"],"prefix":"10.1007","author":[{"given":"Fatima","family":"Haouari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamer","family":"Elsayed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Abdul-Mageed, M., Elmadany, A., et al.: ARBERT & MARBERT: deep bidirectional transformers for Arabic. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7088\u20137105 (2021)","DOI":"10.18653\/v1\/2021.acl-long.551"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5516945","volume":"2021","author":"M Al-Yahya","year":"2021","unstructured":"Al-Yahya, M., Al-Khalifa, H., Al-Baity, H., AlSaeed, D., Essam, A.: Arabic fake news detection: comparative study of neural networks and transformer-based approaches. Complexity 2021, 1\u201310 (2021)","journal-title":"Complexity"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Alhindi, T., Alabdulkarim, A., Alshehri, A., Abdul-Mageed, M., Nakov, P.: AraStance: a multi-country and multi-domain dataset of Arabic stance detection for fact checking. In: NLP4IF 2021, p. 57 (2021)","DOI":"10.18653\/v1\/2021.nlp4if-1.9"},{"key":"33_CR4","unstructured":"Ali, Z.S., Mansour, W., Elsayed, T., Al-Ali, A.: AraFacts: the first large Arabic dataset of naturally occurring claims. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop, pp. 231\u2013236 (2021)"},{"key":"33_CR5","unstructured":"Alqurashi, S., Hamoui, B., Alashaikh, A., Alhindi, A., Alanazi, E.: Eating garlic prevents COVID-19 infection: detecting misinformation on the Arabic content of Twitter. arXiv preprint arXiv:2101.05626 (2021)"},{"issue":"3","key":"33_CR6","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.3390\/s22031006","volume":"22","author":"T Alqurashi","year":"2022","unstructured":"Alqurashi, T.: Stance analysis of distance education in the Kingdom of Saudi Arabia during the COVID-19 pandemic using Arabic Twitter data. Sensors 22(3), 1006 (2022)","journal-title":"Sensors"},{"key":"33_CR7","unstructured":"Antoun, W., Baly, F., Hajj, H.: AraBERT: transformer-based model for Arabic language understanding. In: LREC 2020 Workshop Language Resources and Evaluation Conference, 11\u201316 May 2020, p. 9 (2020)"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Bai, N., Meng, F., Rui, X., Wang, Z.: A multi-task attention tree neural net for stance classification and rumor veracity detection. Appl. Intell. 1\u201311 (2022)","DOI":"10.1007\/s10489-022-03833-5"},{"issue":"5","key":"33_CR9","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1007\/s00607-021-01034-5","volume":"104","author":"N Bai","year":"2022","unstructured":"Bai, N., Meng, F., Rui, X., Wang, Z.: Rumor detection based on a Source-Replies conversation Tree Convolutional Neural Net. Computing 104(5), 1155\u20131171 (2022)","journal-title":"Computing"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Baly, R., Mohtarami, M., Glass, J., M\u00e0rquez, L., Moschitti, A., Nakov, P.: Integrating stance detection and fact checking in a unified corpus. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 2 (Short Papers), pp. 21\u201327. Association for Computational Linguistics, New Orleans, Louisiana, June 2018","DOI":"10.18653\/v1\/N18-2004"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Barr\u00f3n-Cede\u00f1o, A., et al.: The CLEF-2023 CheckThat! Lab: checkworthiness, subjectivity, political bias, factuality, and authority of news articles and their sources. In: Proceedings of the 45th European Conference on Information Retrieval (ECIR 2023) (2023)","DOI":"10.1007\/978-3-031-28241-6_59"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 549\u2013556 (2020)","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Chen, L., Wei, Z., Li, J., Zhou, B., Zhang, Q., Huang, X.J.: Modeling evolution of message interaction for rumor resolution. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 6377\u20136387 (2020)","DOI":"10.18653\/v1\/2020.coling-main.561"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Choi, J., Ko, T., Choi, Y., Byun, H., Kim, C.K.: Dynamic graph convolutional networks with attention mechanism for rumor detection on social media. Plos One 16(8), e0256039 (2021)","DOI":"10.1371\/journal.pone.0256039"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Darwish, K., Magdy, W., Zanouda, T.: Improved stance prediction in a user similarity feature space. In: Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 145\u2013148 (2017)","DOI":"10.1145\/3110025.3110112"},{"key":"33_CR16","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Dougrez-Lewis, J., Kochkina, E., Arana-Catania, M., Liakata, M., He, Y.: PHEMEPlus: enriching social media rumour verification with external evidence. In: Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER), pp. 49\u201358 (2022)","DOI":"10.18653\/v1\/2022.fever-1.6"},{"key":"33_CR18","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/978-3-030-57796-4_25","volume-title":"Advances in Intelligent Networking and Collaborative Systems","author":"MK Elhadad","year":"2021","unstructured":"Elhadad, M.K., Li, K.F., Gebali, F.: COVID-19-FAKES: a Twitter (Arabic\/English) dataset for detecting misleading information on COVID-19. In: Barolli, L., Li, K.F., Miwa, H. (eds.) INCoS 2020. AISC, vol. 1263, pp. 256\u2013268. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-57796-4_25"},{"key":"33_CR19","unstructured":"Haouari, F., Hasanain, M., Suwaileh, R., Elsayed, T.: ArCOV19-rumors: Arabic COVID-19 Twitter dataset for misinformation detection. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop, pp. 72\u201381 (2021)"},{"key":"33_CR20","unstructured":"Hasanain, M., et al.: Overview of CheckThat! 2020 Arabic: automatic identification and verification of claims in social media. In: CLEF (2020)"},{"key":"33_CR21","unstructured":"Inoue, G., Alhafni, B., Baimukan, N., Bouamor, H., Habash, N.: The interplay of variant, size, and task type in Arabic pre-trained language models. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop, pp. 92\u2013104 (2021)"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Jaziriyan, M.M., Akbari, A., Karbasi, H.: ExaASC: a general target-based stance detection corpus in Arabic language. In: 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE), pp. 424\u2013429. IEEE (2021)","DOI":"10.1109\/ICCKE54056.2021.9721486"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Khouja, J.: Stance prediction and claim verification: an Arabic perspective. In: Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER). Association for Computational Linguistics, Seattle, USA (2020)","DOI":"10.18653\/v1\/2020.fever-1.2"},{"key":"33_CR24","doi-asserted-by":"crossref","unstructured":"Kumar, S., Carley, K.: Tree LSTMs with convolution units to predict stance and rumor veracity in social media conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, July 2019","DOI":"10.18653\/v1\/P19-1498"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Lan, W., Chen, Y., Xu, W., Ritter, A.: An empirical study of pre-trained transformers for Arabic information extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 4727\u20134734. Association for Computational Linguistics, Online, November 2020","DOI":"10.18653\/v1\/2020.emnlp-main.382"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wu, Y.F.B.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11268"},{"key":"33_CR27","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., Wong, K.F.: Rumor detection on Twitter with tree-structured recursive neural networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1980\u20131989 (2018)","DOI":"10.18653\/v1\/P18-1184"},{"issue":"6","key":"33_CR28","first-page":"778","volume":"12","author":"AR Mahlous","year":"2021","unstructured":"Mahlous, A.R., Al-Laith, A.: Fake news detection in Arabic tweets during the COVID-19 pandemic. Int. J. Adv. Comput. Sci. Appl. 12(6), 778\u2013788 (2021)","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"issue":"3","key":"33_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102927","volume":"59","author":"S Roy","year":"2022","unstructured":"Roy, S., Bhanu, M., Saxena, S., Dandapat, S., Chandra, J.: gDART: improving rumor verification in social media with discrete attention representations. Inf. Process. Manage. 59(3), 102927 (2022)","journal-title":"Inf. Process. Manage."},{"key":"33_CR30","doi-asserted-by":"crossref","unstructured":"Safaya, A., Abdullatif, M., Yuret, D.: KUISAIL at SemEval-2020 task 12: BERT-CNN for offensive speech identification in social media. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 2054\u20132059. International Committee for Computational Linguistics, Barcelona (Online), December 2020","DOI":"10.18653\/v1\/2020.semeval-1.271"},{"key":"33_CR31","doi-asserted-by":"crossref","unstructured":"Sawan, A., Thaher, T., Abu-el-rub, N.: Sentiment analysis model for fake news identification in Arabic tweets. In: 2021 IEEE 15th International Conference on Application of Information and Communication Technologies (AICT), pp. 1\u20136 (2021)","DOI":"10.1109\/AICT52784.2021.9620509"},{"issue":"6","key":"33_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102712","volume":"58","author":"C Song","year":"2021","unstructured":"Song, C., Shu, K., Wu, B.: Temporally evolving graph neural network for fake news detection. Inf. Process. Manage. 58(6), 102712 (2021)","journal-title":"Inf. Process. Manage."},{"key":"33_CR33","doi-asserted-by":"crossref","unstructured":"Wu, L., Rao, Y., Jin, H., Nazir, A., Sun, L.: Different absorption from the same sharing: sifted multi-task learning for fake news detection. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, November 2019","DOI":"10.18653\/v1\/D19-1471"},{"key":"33_CR34","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, J., Khoo, L.M.S., Chieu, H.L., Xia, R.: Coupled hierarchical transformer for stance-aware rumor verification in social media conversations. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1392\u20131401. Association for Computational Linguistics, Online, November 2020","DOI":"10.18653\/v1\/2020.emnlp-main.108"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28238-6_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T13:49:22Z","timestamp":1709646562000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28238-6_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031282379","9783031282386"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28238-6_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","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":"2 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"45","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2023.org\/index.html?v=1.0","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":"489","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":"77","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":"83","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":"16% - 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)"}}]}}