{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:40:08Z","timestamp":1743061208844,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031635359"},{"type":"electronic","value":"9783031635366"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"vor","delay-in-days":198,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The increasing usage of social networks has led to a growing number of discussions on the Internet that are a valuable source of argumentation that occurs in real time. Such conversations are often made up of a large number of participants and are characterized by a fast pace. Platforms like X\/Twitter and Hacker News (HN) allow users to respond to other users\u2019 posts, leading to a tree-like structure. Previous work focused on training supervised models on datasets obtained from debate portals like Kialo where authors provide polarity labels (i.e., support\/attack) together with their posts. Such classifiers may yield suboptimal predictions for the noisier posts from X or HN, so we propose unsupervised prompting strategies for large language models instead. Our experimental evaluation found this approach to be more effective for X conversations than a model fine-tuned on Kialo debates, but less effective for HN posts (which are more technical and less argumentative). Finally, we provide an open-source application for converting discussions on these platforms into argument graphs.<\/jats:p>","DOI":"10.1007\/978-3-031-63536-6_7","type":"book-chapter","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T05:01:50Z","timestamp":1721106110000},"page":"108-126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PolArg: Unsupervised Polarity Prediction of\u00a0Arguments in\u00a0Real-Time Online Conversations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7720-0436","authenticated-orcid":false,"given":"Mirko","family":"Lenz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5515-7158","authenticated-orcid":false,"given":"Ralph","family":"Bergmann","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"issue":"1","key":"7_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3624579","volume":"18","author":"V Agarwal","year":"2023","unstructured":"Agarwal, V., Young, A.P., Joglekar, S., Sastry, N.: A graph-based context-aware model to understand online conversations. ACM Trans. Web 18(1), 1\u201327 (2023)","journal-title":"ACM Trans. Web"},{"key":"7_CR2","unstructured":"Allen, J.F.: Natural language processing. In: Encyclopedia of Computer Science (2003)"},{"key":"7_CR3","unstructured":"Bosc, T., Cabrio, E., Villata, S.: DART: a dataset of arguments and their relations on Twitter. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) (2016)"},{"key":"7_CR4","unstructured":"Bosc, T., Cabrio, E., Villata, S.: Tweeties squabbling: positive and negative results in applying argument mining on social media. In: Computational Models of Argument (2016)"},{"key":"7_CR5","unstructured":"Cabrio, E., Villata, S.: Detecting bipolar semantic relations among natural language arguments with textual entailment: a study. In: Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora (2013)"},{"issue":"3","key":"7_CR6","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1080\/19462166.2013.862303","volume":"4","author":"E Cabrio","year":"2013","unstructured":"Cabrio, E., Villata, S.: A natural language bipolar argumentation approach to support users in online debate interactions. Argum. Comput. 4(3), 209\u2013230 (2013)","journal-title":"Argum. Comput."},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Cayrol, C., Lagasquie-Schiex, M.C.: On the acceptability of arguments in bipolar argumentation frameworks. In: Symbolic and Quantitative Approaches to Reasoning with Uncertainty (2005)","DOI":"10.1007\/11518655_33"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Chen, E., Deb, A., Ferrara, E.: #Election2020: the first public Twitter dataset on the 2020 US Presidential election. J. Comput. Soc. Sci. (2021)","DOI":"10.1007\/s42001-021-00117-9"},{"issue":"4","key":"7_CR9","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1017\/S0269888906001044","volume":"21","author":"CI Ches\u00f1evar","year":"2006","unstructured":"Ches\u00f1evar, C.I., et al.: Towards an argument interchange format. Knowl. Eng. Rev. 21(4), 293\u2013316 (2006)","journal-title":"Knowl. Eng. Rev."},{"issue":"1","key":"7_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37\u201346 (1960)","journal-title":"Educ. Psychol. Measur."},{"key":"7_CR11","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 (2018)"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Dusmanu, M., Cabrio, E., Villata, S.: Argument mining on Twitter: arguments, facts and sources. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017)","DOI":"10.18653\/v1\/D17-1245"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Goudas, T., Louizos, C., Petasis, G., Karkaletsis, V.: Argument extraction from news, blogs, and social media. In: Artificial Intelligence: Methods and Applications (2014)","DOI":"10.1007\/978-3-319-07064-3_23"},{"issue":"3","key":"7_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3056539","volume":"17","author":"I Gurevych","year":"2017","unstructured":"Gurevych, I., Lippi, M., Torroni, P.: Argumentation in social media. ACM Trans. Internet Technol. 17(3), 1\u20132 (2017)","journal-title":"ACM Trans. Internet Technol."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"67698","DOI":"10.1109\/ACCESS.2020.2983656","volume":"8","author":"A Karami","year":"2020","unstructured":"Karami, A., Lundy, M., Webb, F., Dwivedi, Y.K.: Twitter and research: a systematic literature review through text mining. IEEE Access 8, 67698\u201367717 (2020)","journal-title":"IEEE Access"},{"issue":"6","key":"7_CR16","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1002\/asi.24007","volume":"69","author":"DZ Korman","year":"2018","unstructured":"Korman, D.Z., Mack, E., Jett, J., Renear, A.H.: Defining textual entailment. J. Assoc. Inf. Sci. Technol. 69(6), 763\u2013772 (2018)","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics (1977)","DOI":"10.2307\/2529310"},{"issue":"4","key":"7_CR18","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1162\/coli_a_00364","volume":"45","author":"J Lawrence","year":"2019","unstructured":"Lawrence, J., Reed, C.: Argument mining: a survey. Comput. Linguist. 45(4), 765\u2013818 (2019)","journal-title":"Comput. Linguist."},{"issue":"2","key":"7_CR19","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/BF02295996","volume":"12","author":"Q McNemar","year":"1947","unstructured":"McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153\u2013157 (1947)","journal-title":"Psychometrika"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s10844-020-00620-x","volume":"56","author":"A Ouertatani","year":"2021","unstructured":"Ouertatani, A., Gasmi, G., Latiri, C.: Parsing argued opinion structure in Twitter content. J. Intell. Inf. Syst. 56, 327\u2013353 (2021)","journal-title":"J. Intell. Inf. Syst."},{"issue":"1","key":"7_CR21","first-page":"1","volume":"7","author":"A Peldszus","year":"2013","unstructured":"Peldszus, A., Stede, M.: From argument diagrams to argumentation mining in texts - a survey. IJCINI 7(1), 1\u201331 (2013)","journal-title":"IJCINI"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: 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) (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"7_CR23","unstructured":"Shevtsov, A., Oikonomidou, M., Antonakaki, D., Pratikakis, P., Ioannidis, S.: Analysis of Twitter and YouTube during USelections 2020. arXiv:2010.08183 (2020)"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Stab, C., Gurevych, I.: Identifying argumentative discourse structures in persuasive essays. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)","DOI":"10.3115\/v1\/D14-1006"},{"issue":"3","key":"7_CR25","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1162\/COLI_a_00295","volume":"43","author":"C Stab","year":"2017","unstructured":"Stab, C., Gurevych, I.: Parsing argumentation structures in persuasive essays. Comput. Linguist. 43(3), 619\u2013659 (2017)","journal-title":"Comput. Linguist."},{"key":"7_CR26","unstructured":"Touvron, H., et\u00a0al.: Llama 2: Open Foundation and Fine-Tuned Chat Models (2023)"},{"key":"7_CR27","unstructured":"Wang, S., Fang, H., Khabsa, M., Mao, H., Ma, H.: Entailment as Few-Shot Learner. arXiv:2104.14690 (2021)"}],"container-title":["Lecture Notes in Computer Science","Robust Argumentation Machines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63536-6_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T05:03:18Z","timestamp":1721106198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63536-6_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031635359","9783031635366"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63536-6_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RATIO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference on Advances in Robust Argumentation Machines","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bielefeld","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ratio2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ratio.sc.cit-ec.uni-bielefeld.de\/de\/home\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}