{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T01:13:56Z","timestamp":1783386836542,"version":"3.54.6"},"publisher-location":"Cham","reference-count":60,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032314062","type":"print"},{"value":"9783032314079","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-31407-9_16","type":"book-chapter","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:15:08Z","timestamp":1783383308000},"page":"248-266","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthesizing Public Opinion: Can LLMs Generate Reliable Summaries from\u00a0Social Media Discourse for\u00a0Predictive Modeling?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7305-1429","authenticated-orcid":false,"given":"Ivan","family":"Blekanov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9473-6179","authenticated-orcid":false,"given":"Nikita","family":"Tarasov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9713-4528","authenticated-orcid":false,"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0740-561X","authenticated-orcid":false,"given":"Svetlana S.","family":"Bodrunova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,7]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Habermas, J.: Between Facts and Norms: Contributions to a Discursive Theory of Law and Democracy (1996)","DOI":"10.7551\/mitpress\/1564.001.0001"},{"issue":"4","key":"16_CR2","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1111\/j.1468-2885.2006.00280.x","volume":"16","author":"J Habermas","year":"2006","unstructured":"Habermas, J.: Political communication in media society: does democracy still enjoy an epistemic dimension? The impact of normative theory on empirical research. Commun. Theory 16(4), 411\u2013426 (2006)","journal-title":"Commun. Theory"},{"key":"16_CR3","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/2324.001.0001","volume-title":"Deliberative Democracy","author":"J Bohman","year":"1997","unstructured":"Bohman, J., Rehg, W.: Deliberative Democracy, vol. 997. MIT Press, Cambridge, MA (1997)"},{"issue":"1\u20132","key":"16_CR4","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1080\/13183222.2018.1423942","volume":"25","author":"B Pfetsch","year":"2018","unstructured":"Pfetsch, B.: Dissonant and disconnected public spheres as challenge for political communication research. Javnost-The Public 25(1\u20132), 59\u201365 (2018)","journal-title":"Javnost-The Public"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Papacharissi, Z.: Affective Publics: Sentiment, Technology, and Politics. Oxford University Press (2015)","DOI":"10.1093\/acprof:oso\/9780199999736.001.0001"},{"issue":"5","key":"16_CR6","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1177\/0270467610380011","volume":"30","author":"S Yardi","year":"2010","unstructured":"Yardi, S., Boyd, D.: Dynamic debates: an analysis of group polarization over time on twitter. Bull. Sci. Technol. Soc. 30(5), 316\u2013327 (2010)","journal-title":"Bull. Sci. Technol. Soc."},{"key":"16_CR7","doi-asserted-by":"publisher","unstructured":"Bodrunova, S.S., Blekanov, I.S., Tarasov, N.: Cumulative polarization: Patterns of accumulation of neutral and politicized echo chambers on Russian twitter. In: Coman, A., Vasilache, S. (eds.) International Conference on Human-Computer Interaction, pp. 167\u2013182. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-35915-6_13","DOI":"10.1007\/978-3-031-35915-6_13"},{"key":"16_CR8","unstructured":"Grootendorst, M.: BERTopic: neural topic modeling with a class-based Tf-idf procedure. arXiv preprint arXiv:2203.05794 (2022)"},{"key":"16_CR9","doi-asserted-by":"publisher","unstructured":"Kim, D., Oh, A.: Topic chains for understanding a news corpus. In: Gelbukh, A. (ed.) International Conference on Intelligent Text Processing and Computational Linguistics, pp. 163\u2013176. Springer, Berlin, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-19437-5_13","DOI":"10.1007\/978-3-642-19437-5_13"},{"issue":"4","key":"16_CR10","first-page":"915","volume":"67","author":"J Song","year":"2016","unstructured":"Song, J., et al.: Discovering hierarchical topic evolution in time-stamped documents. J. Am. Soc. Inf. Sci. 67(4), 915\u2013927 (2016)","journal-title":"J. Am. Soc. Inf. Sci."},{"issue":"1","key":"16_CR11","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s10618-016-0454-1","volume":"31","author":"P Wang","year":"2017","unstructured":"Wang, P., Zhang, P., Zhou, C., Li, Z., Yang, H.: Hierarchical evolving Dirichlet processes for modeling nonlinear evolutionary traces in temporal data. Data Min. Knowl. Disc. 31(1), 32\u201364 (2017)","journal-title":"Data Min. Knowl. Disc."},{"key":"16_CR12","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"16_CR13","unstructured":"Zhang, J., Zhao, Y., Saleh, M., Liu, P.: Pegasus: pre-training with extracted gap-sentences for abstractive summarization. In: International Conference on Machine Learning, pp. 11328\u201311339. PMLR (2020)"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"issue":"140","key":"16_CR15","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR16","unstructured":"Chung, H.W., et al.: Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416 (2022)"},{"key":"16_CR17","unstructured":"OpenAI, Achiam, J., Adler, S., et\u00a0al.: GPT-4 technical report. arXiv:2303.08774 (2024)"},{"key":"16_CR18","unstructured":"DeepSeek-AI, Liu, A., Feng, B., Wang, B., et\u00a0al.: Deepseek-v2: a strong, economical, and efficient mixture-of-experts language model. arXiv:2405.04434 (2024)"},{"key":"16_CR19","doi-asserted-by":"publisher","unstructured":"Blekanov, I.S., Tarasov, N., Bodrunova, S.S., Sergeev, S.L.: Mapping opinion cumulation: topic modeling-based dynamic summarization of user discussions on social networks. In: Coman, A., Vasilache, S. (eds.) International Conference on Human-Computer Interaction, pp. 25\u201340. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-35915-6_3","DOI":"10.1007\/978-3-031-35915-6_3"},{"key":"16_CR20","doi-asserted-by":"publisher","unstructured":"Bodrunova, S.S., Blekanov, I.S., Tarasov, N.A.: \u2018opinion tree\u2019: a method for mapping online discussions based on neural-network topic modeling and abstractive summarization. Vestnik Moskovskogo Universiteta. Series 10. Journalism 5, 179\u2013208 (2025). https:\/\/doi.org\/10.55959\/msu.vestnik.journ.5.2025.179208","DOI":"10.55959\/msu.vestnik.journ.5.2025.179208"},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 113\u2013120 (2006)","DOI":"10.1145\/1143844.1143859"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Saha, A., Sindhwani, V.: Learning evolving and emerging topics in social media: a dynamic NMF approach with temporal regularization. In: Proceedings of the fifth ACM International Conference on Web Search and Data Mining, pp. 693\u2013702 (2012)","DOI":"10.1145\/2124295.2124376"},{"key":"16_CR23","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/j.neucom.2019.11.077","volume":"383","author":"W Gao","year":"2020","unstructured":"Gao, W., et al.: Generation of topic evolution graphs from short text streams. Neurocomputing 383, 282\u2013294 (2020)","journal-title":"Neurocomputing"},{"issue":"2","key":"16_CR24","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1111\/j.1460-2466.1974.tb00367.x","volume":"24","author":"E Noelle-Neumann","year":"1974","unstructured":"Noelle-Neumann, E.: The spiral of silence a theory of public opinion. J. Commun. 24(2), 43\u201351 (1974)","journal-title":"J. Commun."},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"King, A.A., Anderson, F.D.: Nixon, agnew, and the \u201csilent majority\u201d: a case study in the rhetoric of polarization. Western Speech 35(4), 243\u2013255 (1971)","DOI":"10.1080\/10570317109373712"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Perse, E.M.: Media Effects and Society (2001), mahwah, nj: L","DOI":"10.4324\/9781410600820"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Koch, T., Arendt, F., Maximilian, L.: Media effects: cumulation and duration. Int. Encycl. Media Effects 1\u201311 (2017)","DOI":"10.1002\/9781118783764.wbieme0217"},{"key":"16_CR28","unstructured":"Mouffe, C.: The Democratic Paradox. Verso (2000)"},{"key":"16_CR29","unstructured":"Allan, J., Carbonell, J.G., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study final report (1998)"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Alam, M.H., Ryu, W.J., Lee, S.: Hashtag-based topic evolution in social media. World Wide Web 20(6), 1527\u20131549 (2017)","DOI":"10.1007\/s11280-017-0451-3"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Mao, W., Lin, J.: Modeling topic evolution in social media short texts. In: 2017 IEEE International Conference on Big Knowledge (ICBK), pp. 315\u2013319. IEEE (2017)","DOI":"10.1109\/ICBK.2017.39"},{"issue":"1","key":"16_CR32","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/s10618-021-00804-1","volume":"36","author":"X Lu","year":"2022","unstructured":"Lu, X., Guo, Y., Chen, J., Wang, F.: Topic change point detection using a mixed bayesian model. Data Min. Knowl. Disc. 36(1), 146\u2013173 (2022)","journal-title":"Data Min. Knowl. Disc."},{"issue":"2","key":"16_CR33","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s11280-016-0390-4","volume":"20","author":"J Huang","year":"2017","unstructured":"Huang, J., Peng, M., Wang, H., Cao, J., Gao, W., Zhang, X.: A probabilistic method for emerging topic tracking in microblog stream. World Wide Web 20(2), 325\u2013350 (2017)","journal-title":"World Wide Web"},{"issue":"1","key":"16_CR34","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10844-017-0487-y","volume":"51","author":"L Chen","year":"2018","unstructured":"Chen, L., Zhang, H., Jose, J.M., Yu, H., Moshfeghi, Y., Triantafillou, P.: Topic detection and tracking on heterogeneous information. J. Intell. Inf. Syst. 51(1), 115\u2013137 (2018)","journal-title":"J. Intell. Inf. Syst."},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"Hong, L., Yin, D., Guo, J., Davison, B.D.: Tracking trends: incorporating term volume into temporal topic models. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 484\u2013492 (2011)","DOI":"10.1145\/2020408.2020485"},{"key":"16_CR36","unstructured":"Lee, P., Lakshmanan, L.V., Milios, E.E.: Event evolution tracking from streaming social posts. arXiv preprint arXiv:1311.5978 (2013)"},{"key":"16_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1007\/978-3-642-19656-0_41","volume-title":"Social Computing, Behavioral-Cultural Modeling and Prediction","author":"X Tang","year":"2011","unstructured":"Tang, X., Yang, C.C.: Following the social media: aspect evolution of online discussion. In: Salerno, J., Yang, S.J., Nau, D., Chai, S.-K. (eds.) SBP 2011. LNCS, vol. 6589, pp. 292\u2013300. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-19656-0_41"},{"key":"16_CR38","doi-asserted-by":"crossref","unstructured":"Lee, P., Lakshmanan, L.V., Milios, E.E.: Incremental cluster evolution tracking from highly dynamic network data. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 3\u201314. IEEE (2014)","DOI":"10.1109\/ICDE.2014.6816635"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Dermouche, M., Velcin, J., Khouas, L., Loudcher, S.: A joint model for topic-sentiment evolution over time. In: 2014 IEEE International Conference on Data Mining, pp. 773\u2013778. IEEE (2014)","DOI":"10.1109\/ICDM.2014.82"},{"issue":"4","key":"16_CR40","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1016\/j.ipm.2016.10.004","volume":"53","author":"P Srijith","year":"2017","unstructured":"Srijith, P., Hepple, M., Bontcheva, K., Preotiuc-Pietro, D.: Sub-story detection in twitter with hierarchical Dirichlet processes. Inf. Process. Manage. 53(4), 989\u20131003 (2017)","journal-title":"Inf. Process. Manage."},{"key":"16_CR41","unstructured":"Kawamae, N.: Topic chronicle forest for topic discovery and tracking. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 315\u2013323 (2018)"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Momeni, E., Karunasekera, S., Goyal, P., Lerman, K.: Modeling evolution of topics in large-scale temporal text corpora. In: Proceedings of the International AAAI Conference on Web and Social Media, vol.\u00a012 (2018)","DOI":"10.1609\/icwsm.v12i1.15068"},{"key":"16_CR43","doi-asserted-by":"publisher","first-page":"64847","DOI":"10.1109\/ACCESS.2018.2878494","volume":"6","author":"M Abulaish","year":"2018","unstructured":"Abulaish, M., Fazil, M.: Modeling topic evolution in twitter: an embedding-based approach. IEEE Access 6, 64847\u201364857 (2018)","journal-title":"IEEE Access"},{"issue":"11","key":"16_CR44","doi-asserted-by":"publisher","first-page":"7359","DOI":"10.1007\/s11192-023-04776-5","volume":"129","author":"Z Wang","year":"2024","unstructured":"Wang, Z., Chen, J., Chen, J., Chen, H.: Identifying interdisciplinary topics and their evolution based on bertopic. Scientometrics 129(11), 7359\u20137384 (2024)","journal-title":"Scientometrics"},{"key":"16_CR45","unstructured":"Li, J., Li, S.: Evolutionary hierarchical Dirichlet process for timeline summarization. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 556\u2013560 (2013)"},{"key":"16_CR46","unstructured":"Brigadir, I., Greene, D., Cunningham, P.: Adaptive representations for tracking breaking news on twitter. arXiv preprint arXiv:1403.2923 (2014)"},{"issue":"5","key":"16_CR47","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1109\/TKDE.2014.2345379","volume":"27","author":"Z Wang","year":"2014","unstructured":"Wang, Z., Shou, L., Chen, K., Chen, G., Mehrotra, S.: On summarization and timeline generation for evolutionary tweet streams. IEEE Trans. Knowl. Data Eng. 27(5), 1301\u20131315 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"16_CR48","unstructured":"Zhou, Y., Kanhabua, N., Cristea, A.I.: Real-time timeline summarisation for high-impact events in twitter. In: Proceedings of the 22nd European Conference on Artificial Intelligence, vol. 285, pp. 1158\u20131166. IOS Press (2016)"},{"issue":"2","key":"16_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2996183","volume":"17","author":"N Alsaedi","year":"2017","unstructured":"Alsaedi, N., Burnap, P., Rana, O.: Can we predict a riot? Disruptive event detection using twitter. ACM Trans. Internet Technol. (TOIT) 17(2), 1\u201326 (2017)","journal-title":"ACM Trans. Internet Technol. (TOIT)"},{"key":"16_CR50","unstructured":"Chang, Y., Tang, J., Yin, D., Yamada, M., Liu, Y.: Timeline summarization from social media with life cycle models. In: IJCAI, pp. 3698\u20133704 (2016)"},{"issue":"1","key":"16_CR51","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/TCSS.2019.2954116","volume":"7","author":"P Goyal","year":"2019","unstructured":"Goyal, P., Kaushik, P., Gupta, P., Vashisth, D., Agarwal, S., Goyal, N.: Multilevel event detection, storyline generation, and summarization for tweet streams. IEEE Trans. Comput. Soc. Syst. 7(1), 8\u201323 (2019)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"16_CR52","doi-asserted-by":"crossref","unstructured":"Blekanov, I.S., Tarasov, N., Bodrunova, S.S.: Transformer-based abstractive summarization for reddit and twitter: single posts vs. comment pools in three languages. Future Internet 14(3), 69 (2022)","DOI":"10.3390\/fi14030069"},{"key":"16_CR53","unstructured":"Li, Z., Zhang, X., Zhang, Y., Long, D., Xie, P., Zhang, M.: Towards general text embeddings with multi-stage contrastive learning. arXiv preprint arXiv:2308.03281 (2023)"},{"key":"16_CR54","unstructured":"Team, G., et\u00a0al.: Gemma: open models based on Gemini research and technology. arXiv preprint arXiv:2403.08295 (2024)"},{"key":"16_CR55","unstructured":"Jiang, A.Q., Sablayrolles, A., Mensch, A., et\u00a0al.: Mistral 7b. arXiv:2310.06825 (2023)"},{"key":"16_CR56","unstructured":"Bai, J., et\u00a0al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)"},{"key":"16_CR57","unstructured":"Makice, K.: Twitter API: Up and Running: Learn How to Build Applications with the Twitter API. O\u2019Reilly Media, Inc. (2009)"},{"key":"16_CR58","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: evaluating text generation with BERT. arXiv preprint arXiv:1904.09675 (2019)"},{"key":"16_CR59","doi-asserted-by":"crossref","unstructured":"Scialom, T., et al.: Questeval: summarization asks for fact-based evaluation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6594\u20136604 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.529"},{"key":"16_CR60","doi-asserted-by":"crossref","unstructured":"Yang, Y., Li, Z., Dong, Q., Xia, H., Sui, Z.: Can large multimodal models uncover deep semantics behind images? arXiv preprint arXiv:2402.11281 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.113"}],"container-title":["Lecture Notes in Computer Science","Social Computing and Social Media"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-31407-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:16:09Z","timestamp":1783383369000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-31407-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032314062","9783032314079"],"references-count":60,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-31407-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montreal, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}