{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T17:18:09Z","timestamp":1763572689931,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031243394"},{"type":"electronic","value":"9783031243400"}],"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\/978-3-031-24340-0_17","type":"book-chapter","created":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T13:03:43Z","timestamp":1677330223000},"page":"222-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-task Learning for\u00a0Detecting Stance in\u00a0Tweets"],"prefix":"10.1007","author":[{"given":"Devamanyu","family":"Hazarika","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gangeshwar","family":"Krishnamurthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soujanya","family":"Poria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roger","family":"Zimmermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,26]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Rajagopalan, S., Srikant, R., Xu, Y.: Mining newsgroups using networks arising from social behavior. In: Proceedings of the 12th International Conference on World Wide Web, pp. 529\u2013535. ACM (2003)","DOI":"10.1145\/775152.775227"},{"key":"17_CR2","unstructured":"Anand, P., Walker, M., Abbott, R., Tree, J.E.F., Bowmani, R., Minor, M.: Cats rule and dogs drool!: classifying stance in online debate. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 1\u20139. Association for Computational Linguistics (2011)"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Augenstein, I., Rockt\u00e4schel, T., Vlachos, A., Bontcheva, K.: Stance detection with bidirectional conditional encoding. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 876\u2013885 (2016)","DOI":"10.18653\/v1\/D16-1084"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., Bengio, Y.: End-to-end attention-based large vocabulary speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4945\u20134949. IEEE (2016)","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"17_CR5","unstructured":"Chen, W.F., Ku, L.W.: Utcnn: a deep learning model of stance classification on social media text. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1635\u20131645 (2016)"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160\u2013167. ACM (2008)","DOI":"10.1145\/1390156.1390177"},{"key":"17_CR7","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493\u20132537 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Deng, L., Hinton, G., Kingsbury, B.: New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8599\u20138603. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6639344"},{"key":"17_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/978-3-319-76941-7_40","volume-title":"Advances in Information Retrieval","author":"K Dey","year":"2018","unstructured":"Dey, K., Shrivastava, R., Kaushik, S.: Topical stance detection for twitter: a\u00a0two-phase LSTM model using attention. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 529\u2013536. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76941-7_40"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Du, J., Xu, R., He, Y., Gui, L.: Stance classification with target-specific neural attention networks. In: International Joint Conferences on Artificial Intelligence (2017)","DOI":"10.24963\/ijcai.2017\/557"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Hasan, K.S., Ng, V.: Why are you taking this stance? identifying and classifying reasons in ideological debates. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 751\u2013762 (2014)","DOI":"10.3115\/v1\/D14-1083"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Jang, M., Allan, J.: Explaining controversy on social media via stance summarization. arXiv preprint arXiv:1806.07942 (2018)","DOI":"10.1145\/3209978.3210143"},{"key":"17_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-319-62434-1_13","volume-title":"Advances in Computational Intelligence","author":"M Lai","year":"2017","unstructured":"Lai, M., Hern\u00e1ndez Far\u00edas, D.I., Patti, V., Rosso, P.: Friends and enemies of clinton and trump: using context for detecting stance in political tweets. In: Sidorov, G., Herrera-Alc\u00e1ntara, O. (eds.) MICAI 2016. LNCS (LNAI), vol. 10061, pp. 155\u2013168. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-62434-1_13"},{"key":"17_CR15","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111\u20133119 (2013)"},{"key":"17_CR16","unstructured":"Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: A dataset for detecting stance in tweets. In: LREC (2016)"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., Cherry, C.: Semeval-2016 task 6: detecting stance in tweets. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 31\u201341 (2016)","DOI":"10.18653\/v1\/S16-1003"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Internet Technol. (TOIT) 17(3), 26 (2017)","DOI":"10.1145\/3003433"},{"key":"17_CR19","unstructured":"Murakami, A., Raymond, R.: Support or oppose?: classifying positions in online debates from reply activities and opinion expressions. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 869\u2013875. Association for Computational Linguistics (2010)"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Persing, I., Ng, V.: Modeling stance in student essays. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 2174\u20132184 (2016)","DOI":"10.18653\/v1\/P16-1205"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Poddar, L., Hsu, W., Lee, M.L., Subramaniyam, S.: Predicting stances in twitter conversations for detecting veracity of rumors: a neural approach. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 65\u201372. IEEE (2018)","DOI":"10.1109\/ICTAI.2018.00021"},{"key":"17_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/978-3-319-05579-4_19","volume-title":"Social Computing, Behavioral-Cultural Modeling and Prediction","author":"A Rajadesingan","year":"2014","unstructured":"Rajadesingan, A., Liu, H.: Identifying users with opposing opinions in Twitter debates. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds.) SBP 2014. LNCS, vol. 8393, pp. 153\u2013160. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-05579-4_19"},{"key":"17_CR23","unstructured":"Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)"},{"key":"17_CR24","unstructured":"Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on-line debates. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 116\u2013124. Association for Computational Linguistics (2010)"},{"issue":"1","key":"17_CR25","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR26","unstructured":"Sun, Q., Wang, Z., Zhu, Q., Zhou, G.: Stance detection with hierarchical attention network. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2399\u20132409 (2018)"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Thomas, M., Pang, B., Lee, L.: Get out the vote: Determining support or opposition from congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 327\u2013335. Association for Computational Linguistics (2006)","DOI":"10.3115\/1610075.1610122"},{"key":"17_CR28","unstructured":"Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4(2), 26\u201331 (2012)"},{"key":"17_CR29","doi-asserted-by":"crossref","unstructured":"Vijayaraghavan, P., Sysoev, I., Vosoughi, S., Roy, D.: Deepstance at semeval-2016 task 6: detecting stance in tweets using character and word-level CNNs. In: Proceedings of SemEval, pp. 413\u2013419 (2016)","DOI":"10.18653\/v1\/S16-1067"},{"key":"17_CR30","unstructured":"Walker, M., Anand, P., Abbott, R., Grant, R.: Stance classification using dialogic properties of persuasion. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 592\u2013596. Association for Computational Linguistics (2012)"}],"container-title":["Lecture Notes in Computer Science","Computational Linguistics and Intelligent Text Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-24340-0_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,25]],"date-time":"2023-02-25T13:06:38Z","timestamp":1677330398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-24340-0_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031243394","9783031243400"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-24340-0_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICLing","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Linguistics and Intelligent Text Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"La Rochelle","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicling2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cicling.org\/2019\/","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":"335","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":"95","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":"0","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":"28% - 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":"2.3","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":"3.4","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)"}}]}}