{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:44Z","timestamp":1750220324909,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,14]]},"DOI":"10.1145\/3486622.3493962","type":"proceedings-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T01:18:53Z","timestamp":1649899133000},"page":"226-233","source":"Crossref","is-referenced-by-count":0,"title":["How to Churn Deep Contextual Models?"],"prefix":"10.1145","author":[{"given":"Mohammad Rashedul","family":"Hasan","sequence":"first","affiliation":[{"name":"School of Computing, University of Nebraska-Lincoln, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Beltagy Iz","year":"1865","unstructured":"Iz Beltagy , Kyle Lo , and Arman Cohan . 2019. SciBERT: A Pretrained Language Model for Scientific Text . 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, 3615\u20133620. https:\/\/doi.org\/10. 1865 3\/v1\/D19-1371 Iz Beltagy, Kyle Lo, and Arman Cohan. 2019. SciBERT: A Pretrained Language Model for Scientific Text. 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, 3615\u20133620. https:\/\/doi.org\/10.18653\/v1\/D19-1371"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.485"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2021.644801"},{"key":"e_1_3_2_1_4_1","unstructured":"Limeng Cui and Dongwon Lee. 2020. CoAID: COVID-19 Healthcare Misinformation Dataset. (2020). arxiv:2006.00885\u00a0[cs.SI] Dataset is available at: https:\/\/github.com\/cuilimeng\/CoAID.  Limeng Cui and Dongwon Lee. 2020. CoAID: COVID-19 Healthcare Misinformation Dataset. (2020). arxiv:2006.00885\u00a0[cs.SI] Dataset is available at: https:\/\/github.com\/cuilimeng\/CoAID."},{"key":"e_1_3_2_1_5_1","first-page":"19","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171\u20134186. https:\/\/doi.org\/10. 18653\/v1\/N 19 - 1423 Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.nlpcovid19-2.11"},{"key":"e_1_3_2_1_7_1","unstructured":"N.\u00a0F. Johnson N. Velasquez O.\u00a0K. Jha H. Niyazi R. Leahy N.\u00a0Johnson Restrepo R. Sear P. Manrique Y. Lupu P. Devkota and S. Wuchty. 2020. Covid-19 infodemic reveals new tipping point epidemiology and a revised R formula. arxiv:2008.08513\u00a0[physics.soc-ph]  N.\u00a0F. Johnson N. Velasquez O.\u00a0K. Jha H. Niyazi R. Leahy N.\u00a0Johnson Restrepo R. Sear P. Manrique Y. Lupu P. Devkota and S. Wuchty. 2020. Covid-19 infodemic reveals new tipping point epidemiology and a revised R formula. arxiv:2008.08513\u00a0[physics.soc-ph]"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_2_1_9_1","volume-title":"Sentiment Analysis on COVID Tweets Using COVID-Twitter-BERT with Auxiliary Sentence Approach","author":"Lin Hung\u00a0Yeh","year":"2074","unstructured":"Hung\u00a0Yeh Lin and Teng-Sheng Moh . 2021. Sentiment Analysis on COVID Tweets Using COVID-Twitter-BERT with Auxiliary Sentence Approach . Association for Computing Machinery , New York, NY, USA , 234\u2013238. https:\/\/doi-org.libproxy.unl.edu\/10.1145\/3409334.345 2074 Hung\u00a0Yeh Lin and Teng-Sheng Moh. 2021. Sentiment Analysis on COVID Tweets Using COVID-Twitter-BERT with Auxiliary Sentence Approach. Association for Computing Machinery, New York, NY, USA, 234\u2013238. https:\/\/doi-org.libproxy.unl.edu\/10.1145\/3409334.3452074"},{"key":"e_1_3_2_1_10_1","unstructured":"Shahan\u00a0Ali Memon and Kathleen\u00a0M. Carley. 2020. Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset. (2020). arxiv:2008.00791\u00a0[cs.SI]  Shahan\u00a0Ali Memon and Kathleen\u00a0M. Carley. 2020. Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset. (2020). arxiv:2008.00791\u00a0[cs.SI]"},{"key":"e_1_3_2_1_11_1","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. CoRR abs\/1301.3781(2013). http:\/\/dblp.uni-trier.de\/db\/journals\/corr\/corr1301.html#abs-1301-3781  Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. CoRR abs\/1301.3781(2013). http:\/\/dblp.uni-trier.de\/db\/journals\/corr\/corr1301.html#abs-1301-3781"},{"key":"e_1_3_2_1_12_1","unstructured":"Shervin Minaee Nal Kalchbrenner Erik Cambria Narjes Nikzad Meysam Chenaghlu and Jianfeng Gao. 2021. Deep Learning Based Text Classification: A Comprehensive Review. arxiv:2004.03705\u00a0[cs.CL]  Shervin Minaee Nal Kalchbrenner Erik Cambria Narjes Nikzad Meysam Chenaghlu and Jianfeng Gao. 2021. Deep Learning Based Text Classification: A Comprehensive Review. arxiv:2004.03705\u00a0[cs.CL]"},{"key":"e_1_3_2_1_13_1","unstructured":"Martin M\u00fcller Marcel Salath\u00e9 and Per\u00a0Egil Kummervold. 2020. COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter. CoRR abs\/2005.07503(2020). arxiv:2005.07503https:\/\/arxiv.org\/abs\/2005.07503  Martin M\u00fcller Marcel Salath\u00e9 and Per\u00a0Egil Kummervold. 2020. COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter. CoRR abs\/2005.07503(2020). arxiv:2005.07503https:\/\/arxiv.org\/abs\/2005.07503"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-4302"},{"key":"e_1_3_2_1_17_1","unstructured":"Yifan Qiao Chenyan Xiong Zhenghao Liu and Zhiyuan Liu. 2019. Understanding the Behaviors of BERT in Ranking. CoRR abs\/1904.07531(2019). arxiv:1904.07531http:\/\/arxiv.org\/abs\/1904.07531  Yifan Qiao Chenyan Xiong Zhenghao Liu and Zhiyuan Liu. 2019. Understanding the Behaviors of BERT in Ranking. CoRR abs\/1904.07531(2019). arxiv:1904.07531http:\/\/arxiv.org\/abs\/1904.07531"},{"key":"e_1_3_2_1_18_1","unstructured":"Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai and Xuanjing Huang. 2020. Pre-trained Models for Natural Language Processing: A Survey. arxiv:2003.08271\u00a0[cs.CL]  Xipeng Qiu Tianxiang Sun Yige Xu Yunfan Shao Ning Dai and Xuanjing Huang. 2020. Pre-trained Models for Natural Language Processing: A Survey. arxiv:2003.08271\u00a0[cs.CL]"},{"key":"e_1_3_2_1_19_1","unstructured":"Alec Radford and Ilya Sutskever. 2018. Improving Language Understanding by Generative Pre-Training. In arxiv.  Alec Radford and Ilya Sutskever. 2018. Improving Language Understanding by Generative Pre-Training. In arxiv."},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Reimers Nils","year":"1865","unstructured":"Nils Reimers and Iryna Gurevych . 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks . 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, 3982\u20133992. https:\/\/doi.org\/10. 1865 3\/v1\/D19-1410 Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. 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, 3982\u20133992. https:\/\/doi.org\/10.18653\/v1\/D19-1410"},{"key":"e_1_3_2_1_21_1","volume-title":"Luca\u00a0Zanotti andAlaa Ghoneim, and Jacob\u00a0N. Shapiro","author":"Siwakoti Samikshya","year":"2021","unstructured":"Samikshya Siwakoti , Kamya Yadav , Isra Thange , Nicola Bariletto , Luca\u00a0Zanotti andAlaa Ghoneim, and Jacob\u00a0N. Shapiro . 2021 . Localized Misinformation in a Global Pandemic : Report on COVID-19 Narratives around the World. Empirical Study of Conflict( 2021), 1\u201368. https:\/\/esoc.princeton.edu\/publications\/localized-misinformation-global-pandemic-report-covid-19-narratives-around-world Samikshya Siwakoti, Kamya Yadav, Isra Thange, Nicola Bariletto, Luca\u00a0Zanotti andAlaa Ghoneim, and Jacob\u00a0N. Shapiro. 2021. Localized Misinformation in a Global Pandemic: Report on COVID-19 Narratives around the World. Empirical Study of Conflict(2021), 1\u201368. https:\/\/esoc.princeton.edu\/publications\/localized-misinformation-global-pandemic-report-covid-19-narratives-around-world"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100124"},{"key":"e_1_3_2_1_23_1","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan\u00a0 N. Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017 . Attention is All you Need . In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017 , December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna\u00a0M. Wallach, Rob Fergus, S.\u00a0V.\u00a0N. Vishwanathan, and Roman Garnett (Eds.). 5998\u20136008. https:\/\/proceedings.neurips.cc\/paper\/ 2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan\u00a0N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna\u00a0M. Wallach, Rob Fergus, S.\u00a0V.\u00a0N. Vishwanathan, and Roman Garnett (Eds.). 5998\u20136008. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5446"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.6"}],"event":{"name":"WI-IAT '21: IEEE\/WIC\/ACM International Conference on Web Intelligence","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence"],"location":"ESSENDON VIC Australia","acronym":"WI-IAT '21"},"container-title":["IEEE\/WIC\/ACM International Conference on Web Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3486622.3493962","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3486622.3493962","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:05Z","timestamp":1750191125000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3486622.3493962"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,14]]},"references-count":25,"alternative-id":["10.1145\/3486622.3493962","10.1145\/3486622"],"URL":"https:\/\/doi.org\/10.1145\/3486622.3493962","relation":{},"subject":[],"published":{"date-parts":[[2021,12,14]]}}}