{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:31:19Z","timestamp":1749270679407,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T00:00:00Z","timestamp":1725235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007065","name":"Universit\u00e0 degli Studi di Salerno","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007065","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The topic of persuasion in online conversations has social, political and security implications; as a consequence, the problem of predicting persuasive comments in online discussions is receiving increasing attention in the literature. Following recent advancements in graph neural networks, we analyze the impact of conversation structure in predicting persuasive comments in online discussions. We evaluate the performance of artificial intelligence models receiving as input graphs constructed on the top of online conversations sourced from the \u201cChange My View\u201d Reddit channel. We experiment with different graph architectures and compare the performance on graph neural networks, as structure-based models, and dense neural networks as baseline models. Experiments are conducted on two tasks: (1) persuasive comment detection, aiming to predict which comments are persuasive, and (2) influence prediction, aiming to predict which users are persuasive. The experimental results show that the role of the conversation structure in predicting persuasiveness is strongly dependent on its graph representation given as input to the graph neural network. In particular, a graph structure linking only comments belonging to the same speaker in the conversation achieves the best performance in both tasks. This structure outperforms both the baseline model, which does not consider any structural information, and structures linking different speakers\u2019 comments with each other. Specifically, the F1 score of the best performing model is 0.58, which represents an improvement of 5.45% over the baseline model (F1 score of 0.55) and 7.41% over the model linking different speakers\u2019 comments (F1 score of 0.54).<\/jats:p>","DOI":"10.1007\/s12652-024-04841-8","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T17:02:00Z","timestamp":1725296520000},"page":"3719-3732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Analyzing the impact of conversation structure on predicting persuasive comments online"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0862-3643","authenticated-orcid":false,"given":"Nicola","family":"Capuano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Meyer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco David","family":"Nota","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,2]]},"reference":[{"key":"4841_CR1","doi-asserted-by":"crossref","unstructured":"Al\u00a0Khatib K, V\u00f6lske M, Syed S et\u00a0al (2020) Exploiting personal characteristics of debaters for predicting persuasiveness. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp 7067\u20137072","DOI":"10.18653\/v1\/2020.acl-main.632"},{"key":"4841_CR2","doi-asserted-by":"crossref","unstructured":"Barbieri F, Camacho-Collados J, Neves L et\u00a0al (2020) Tweeteval: unified benchmark and comparative evaluation for tweet classification. arXiv:2010.12421","DOI":"10.18653\/v1\/2020.findings-emnlp.148"},{"issue":"9","key":"4841_CR3","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1177\/1461444815616224","volume":"18","author":"T Diehl","year":"2016","unstructured":"Diehl T, Weeks BE, Gil de Z\u00fa\u00f1iga H (2016) Political persuasion on social media: tracing direct and indirect effects of news use and social interaction. New Media Soc 18(9):1875\u20131895","journal-title":"New Media Soc"},{"key":"4841_CR4","doi-asserted-by":"crossref","unstructured":"Egawa R, Morio G, Fujita K (2019) Annotating and analyzing semantic role of elementary units and relations in online persuasive arguments. In: Proceedings of the 57th annual meeting of the association for computational linguistics: student research workshop, pp 422\u2013428","DOI":"10.18653\/v1\/P19-2059"},{"key":"4841_CR5","unstructured":"Fdnphd (2023) Github\u2014fdnphd\/cmv-structures-role. https:\/\/github.com\/fdnphd\/cmv-structures-role"},{"key":"4841_CR6","doi-asserted-by":"crossref","unstructured":"Fey M, Lenssen JE, Weichert F et\u00a0al (2018) Splinecnn: fast geometric deep learning with continuous b-spline kernels. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 869\u2013877","DOI":"10.1109\/CVPR.2018.00097"},{"key":"4841_CR7","doi-asserted-by":"crossref","unstructured":"Ghosal D, Majumder N, Poria S et\u00a0al (2019) Dialoguegcn: a graph convolutional neural network for emotion recognition in conversation. arXiv:1908.11540","DOI":"10.18653\/v1\/D19-1015"},{"issue":"5","key":"4841_CR8","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1080\/01972243.2018.1497743","volume":"34","author":"H Gil de Zuniga","year":"2018","unstructured":"Gil de Zuniga H, Barnidge M, Diehl T (2018) Political persuasion on social media: a moderated moderation model of political discussion disagreement and civil reasoning. Inf Soc 34(5):302\u2013315","journal-title":"Inf Soc"},{"key":"4841_CR9","first-page":"2388","volume":"2020","author":"Z Guo","year":"2020","unstructured":"Guo Z, Zhang Z, Singh M (2020) In opinion holders\u2019 shoes: modeling cumulative influence for view change in online argumentation. Proc Web Conf 2020:2388\u20132399","journal-title":"Proc Web Conf"},{"key":"4841_CR10","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, vol 30"},{"key":"4841_CR11","doi-asserted-by":"crossref","unstructured":"Hidey C, McKeown K (2018) Persuasive influence detection: the role of argument sequencing. In: Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.12003"},{"key":"4841_CR12","doi-asserted-by":"crossref","unstructured":"Jo Y, Poddar S, Jeon B et\u00a0al (2018) Attentive interaction model: modeling changes in view in argumentation. arXiv:1804.00065","DOI":"10.18653\/v1\/N18-1010"},{"key":"4841_CR13","unstructured":"Khazaei T, Xiao L, Mercer R (2017) Writing to persuade: analysis and detection of persuasive discourse. In: IConference 2017 proceedings"},{"key":"4841_CR14","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"4841_CR15","unstructured":"Li G, Xiong C, Thabet A et\u00a0al (2020) Deepergcn: all you need to train deeper gcns. arXiv:2006.07739"},{"key":"4841_CR16","unstructured":"Louppe G (2014) Understanding random forests: from theory to practice. arXiv:1407.7502"},{"key":"4841_CR17","doi-asserted-by":"crossref","unstructured":"Papakonstantinou T, Horne Z (2023) Characteristics of persuasive deltaboard members on reddit\u2019sr\/changemyview","DOI":"10.31234\/osf.io\/5spq9"},{"key":"4841_CR18","doi-asserted-by":"crossref","unstructured":"Petruzzellis F, Bonchi F, Morales GDF et\u00a0al (2023) On the relation between opinion change and information consumption on reddit. In: Proceedings of the international AAAI conference on web and social media, pp 710\u2013719","DOI":"10.1609\/icwsm.v17i1.22181"},{"key":"4841_CR19","unstructured":"Prabhakaran V, Rambow O (2013) Written dialog and social power: manifestations of different types of power in dialog behavior. In: Proceedings of the sixth international joint conference on natural language processing, pp 216\u2013224"},{"issue":"2","key":"4841_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3014164","volume":"17","author":"S Rosenthal","year":"2017","unstructured":"Rosenthal S, Mckeown K (2017) Detecting influencers in multiple online genres. ACM Trans Internet Technol (TOIT) 17(2):1\u201322","journal-title":"ACM Trans Internet Technol (TOIT)"},{"key":"4841_CR21","doi-asserted-by":"crossref","unstructured":"Shmueli-Scheuer M, Herzig J, Konopnicki D et\u00a0al (2019) Detecting persuasive arguments based on author-reader personality traits and their interaction. In: Proceedings of the 27th ACM conference on user modeling, adaptation and personalization, pp 211\u2013215","DOI":"10.1145\/3320435.3320467"},{"issue":"1","key":"4841_CR22","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1108\/eb026526","volume":"28","author":"K Sparck Jones","year":"1972","unstructured":"Sparck Jones K (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 28(1):11\u201321","journal-title":"J Doc"},{"issue":"1","key":"4841_CR23","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1007\/s42001-021-00153-5","volume":"5","author":"VP Ta","year":"2022","unstructured":"Ta VP, Boyd RL, Seraj S et al (2022) An inclusive, real-world investigation of persuasion in language and verbal behavior. J Comput Soc Sci 5(1):883\u2013903","journal-title":"J Comput Soc Sci"},{"key":"4841_CR24","doi-asserted-by":"crossref","unstructured":"Tan C, Niculae V, Danescu-Niculescu-Mizil C et\u00a0al (2016) Winning arguments: interaction dynamics and persuasion strategies in good-faith online discussions. In: Proceedings of the 25th international conference on world wide web, pp 613\u2013624","DOI":"10.1145\/2872427.2883081"},{"key":"4841_CR25","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A et\u00a0al (2017) Graph attention networks. arXiv:1710.10903"},{"key":"4841_CR26","doi-asserted-by":"crossref","unstructured":"Wei Z, Liu Y, Li Y (2016) Is this post persuasive? Ranking argumentative comments in online forum. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: short papers), pp 195\u2013200","DOI":"10.18653\/v1\/P16-2032"},{"key":"4841_CR27","unstructured":"Wiegmann M, Al\u00a0Khatib K, Khanna V et\u00a0al (2022) Analyzing persuasion strategies of debaters on social media. In: 29th international conference on computational linguistics, international committee on computational linguistics, pp 6897\u20136905"},{"key":"4841_CR28","doi-asserted-by":"crossref","unstructured":"Xiao L, Mensah H (2022) How does the thread level of a comment affect its perceived persuasiveness? A reddit study. In: Science and information conference. Springer, pp 800\u2013813","DOI":"10.1007\/978-3-031-10464-0_55"},{"key":"4841_CR29","doi-asserted-by":"crossref","unstructured":"Yang Z, Yang Y, Cer D et\u00a0al (2020) Universal sentence representation learning with conditional masked language model. arXiv:2012.14388","DOI":"10.18653\/v1\/2021.emnlp-main.502"},{"issue":"5","key":"4841_CR30","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1177\/1461444820908530","volume":"23","author":"T Zerback","year":"2021","unstructured":"Zerback T, T\u00f6pfl F, Kn\u00f6pfle M (2021) The disconcerting potential of online disinformation: persuasive effects of astroturfing comments and three strategies for inoculation against them. New Media Soc 23(5):1080\u20131098","journal-title":"New Media Soc"},{"key":"4841_CR31","unstructured":"Zhang J, Carpenter D, Ko M (2013) Online astroturfing: a theoretical perspective"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-024-04841-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-024-04841-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-024-04841-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T09:16:56Z","timestamp":1728119816000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-024-04841-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,2]]},"references-count":31,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["4841"],"URL":"https:\/\/doi.org\/10.1007\/s12652-024-04841-8","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"type":"print","value":"1868-5137"},{"type":"electronic","value":"1868-5145"}],"subject":[],"published":{"date-parts":[[2024,9,2]]},"assertion":[{"value":"17 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no confict of interest. The funding agency had no role in the design of the study; in the collection, analyses, or interpretation of data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}