{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T12:10:05Z","timestamp":1772539805750,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T00:00:00Z","timestamp":1737849600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T00:00:00Z","timestamp":1737849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"IITP","award":["RS-2024-00439139"],"award-info":[{"award-number":["RS-2024-00439139"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00466626"],"award-info":[{"award-number":["RS-2024-00466626"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-025-01083-z","type":"journal-article","created":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T07:50:16Z","timestamp":1737877816000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["I know your stance! Analyzing Twitter users\u2019 political stance on diverse perspectives"],"prefix":"10.1186","volume":"12","author":[{"given":"Jisu","family":"Kim","sequence":"first","affiliation":[]},{"given":"Dongjae","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Eunil","family":"Park","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,26]]},"reference":[{"key":"1083_CR1","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1111\/jcc4.12084","volume":"19","author":"TJ Johnson","year":"2014","unstructured":"Johnson TJ, Kaye BK. Credibility of social network sites for political information among politically interested internet users. J Comput-mediated Commun. 2014;19:957\u201374.","journal-title":"J Comput-mediated Commun"},{"key":"1083_CR2","doi-asserted-by":"crossref","unstructured":"Dey K, Shrivastava R, Kaushik S. Twitter stance detection\u00e2\u20ac\u201da subjectivity and sentiment polarity inspired two-phase approach. In Proceedings of the IEEE International Conference on Data Mining Workshops, 2017. pp. 365\u2013372.","DOI":"10.1109\/ICDMW.2017.53"},{"key":"1083_CR3","doi-asserted-by":"crossref","unstructured":"Yan Y, Chen J, Shyu M-L. Efficient large-scale stance detection in tweets. Deep learning and neural networks: concepts, methodologies, tools, and applications, 2020. pp. 667\u2013683.","DOI":"10.4018\/978-1-7998-0414-7.ch037"},{"key":"1083_CR4","doi-asserted-by":"crossref","unstructured":"Preo\u0163iuc-Pietro D, Liu Y, Hopkins D, Ungar L. Beyond binary labels: political ideology prediction of twitter users. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017. pp. 729\u2013740. volume\u00a01.","DOI":"10.18653\/v1\/P17-1068"},{"key":"1083_CR5","unstructured":"Murthy D. Twitter. Polity Press Cambridge; 2018."},{"key":"1083_CR6","doi-asserted-by":"crossref","unstructured":"Lai M, Patti V, Ruffo G, Rosso P. Stance evolution and twitter interactions in an italian political debate. In Proceedings of the International Conference on Applications of Natural Language to Information Systems, 2018. pp. 15\u201327.","DOI":"10.1007\/978-3-319-91947-8_2"},{"key":"1083_CR7","doi-asserted-by":"crossref","unstructured":"Xi N, Ma D, Liou M, Steinert-Threlkeld Z.\u00a0C, Anastasopoulos J, Joo J. Understanding the political ideology of legislators from social media images. In Proceedings of the International AAAI Conference on Web and Social Media, volume\u00a014, 2020. pp. 726\u2013737.","DOI":"10.1609\/icwsm.v14i1.7338"},{"key":"1083_CR8","doi-asserted-by":"crossref","unstructured":"Aldayel A, Magdy W. Your stance is exposed! analysing possible factors for stance detection on social media. In Proceedings of the ACM International Conference on Human-Computer Interaction, vol 3, 2019. pp. 1\u201320.","DOI":"10.1145\/3359307"},{"key":"1083_CR9","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 International Workshop on Semantic Evaluation. 2016. pp. 31\u201341.","DOI":"10.18653\/v1\/S16-1003"},{"key":"1083_CR10","unstructured":"Shahrezaye M, Papakyriakopoulos O, Serrano J. C.\u00a0M, Hegelich S. Estimating the political orientation of twitter users in homophilic networks. In Proceedings of the International AAAI Spring Symposium on Interpretable AI for Well-being. 2019."},{"key":"1083_CR11","doi-asserted-by":"publisher","unstructured":"Li A, Liang B, Zhao J, Zhang B, Yang M, Xu R. Stance detection on social media with background knowledge. In Bouamor H, Pino J, Bali K, editors. Proceedings of the 2023 conference on empirical methods in natural language processing. Singapore: Association for Computational Linguistics; 2023. pp. 15703\u201315717. https:\/\/aclanthology.org\/2023.emnlp-main.972. https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.972.","DOI":"10.18653\/v1\/2023.emnlp-main.972"},{"key":"1083_CR12","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V. Roberta: a robustly optimized bert pretraining approach. 2019. https:\/\/arxiv.org\/abs\/1907.11692."},{"key":"1083_CR13","doi-asserted-by":"publisher","unstructured":"Nguyen DQ, Vu T, Tuan\u00a0Nguyen A. BERTweet: A pre-trained language model for English tweets. In Liu Q, Schlangen D, editors. Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations. Online: Association for Computational Linguistics. 2020. pp. 9\u201314. https:\/\/aclanthology.org\/2020.emnlp-demos.2. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-demos.2.","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"1083_CR14","doi-asserted-by":"publisher","unstructured":"Shin T, Razeghi Y, IV R. L.\u00a0L, Wallace E, Singh S. AutoPrompt: Eliciting knowledge from language models with automatically generated prompts. In Empirical Methods in Natural Language Processing (EMNLP), 2020. pp. 4222\u20134235. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.346.","DOI":"10.18653\/v1\/2020.emnlp-main.346"},{"key":"1083_CR15","doi-asserted-by":"publisher","unstructured":"Li Y, Sosea T, Sawant A, Nair A.\u00a0J, Inkpen D, Caragea C. P-stance: a large dataset for stance detection in political domain. In Zong C, Xia F, Li W, Navigli R, editors. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Online: Association for Computational Linguistics. 2021. pp. 2355\u20132365. https:\/\/aclanthology.org\/2021.findings-acl.208. https:\/\/doi.org\/10.18653\/v1\/2021.findings-acl.208.","DOI":"10.18653\/v1\/2021.findings-acl.208"},{"key":"1083_CR16","doi-asserted-by":"publisher","unstructured":"Glandt K, Khanal S, Li Y, Caragea D, Caragea C. Stance detection in COVID-19 tweets. In C.\u00a0Zong, F.\u00a0Xia, W.\u00a0Li, & R.\u00a0Navigli (Eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Online: Association for Computational Linguistics. 2021. pp. 1596\u20131611. https:\/\/aclanthology.org\/2021.acl-long.127. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.127.","DOI":"10.18653\/v1\/2021.acl-long.127"},{"key":"1083_CR17","doi-asserted-by":"publisher","unstructured":"Lan X, Gao C, Jin D, Li Y. Stance detection with collaborative role-infused llm-based agents. Proceedings of the International AAAI Conference on Web and Social Media, 18, 891\u2013903. 2024. https:\/\/ojs.aaai.org\/index.php\/ICWSM\/article\/view\/31360. https:\/\/doi.org\/10.1609\/icwsm.v18i1.31360.","DOI":"10.1609\/icwsm.v18i1.31360"},{"key":"1083_CR18","doi-asserted-by":"publisher","unstructured":"Allaway E, McKeown K. Zero-shot stance detection: a dataset and model using generalized topic representations. In B.\u00a0Webber, T.\u00a0Cohn, Y.\u00a0He, & Y.\u00a0Liu (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Online: Association for Computational Linguistics. 2020. pp. 8913\u20138931. https:\/\/aclanthology.org\/2020.emnlp-main.717. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.717.","DOI":"10.18653\/v1\/2020.emnlp-main.717"},{"key":"1083_CR19","doi-asserted-by":"publisher","unstructured":"Peng X, Zhou Z, Zhang C, Xu K (2024). Online social behavior enhanced detection of political stances in tweets. Proceedings of the International AAAI Conference on Web and Social Media, 18, 1207\u20131219. https:\/\/ojs.aaai.org\/index.php\/ICWSM\/article\/view\/31383. https:\/\/doi.org\/10.1609\/icwsm.v18i1.31383.","DOI":"10.1609\/icwsm.v18i1.31383"},{"key":"1083_CR20","doi-asserted-by":"publisher","first-page":"21921","DOI":"10.1109\/ACCESS.2024.3360487","volume":"12","author":"K Wu","year":"2024","unstructured":"Wu K, Zhou Y, Ma J, Guo X. Topic-specific political stance inference in social networks with case studies. IEEE Access. 2024;12:21921\u201335. https:\/\/doi.org\/10.1109\/ACCESS.2024.3360487.","journal-title":"IEEE Access"},{"key":"1083_CR21","doi-asserted-by":"publisher","unstructured":"Das R, Singh TD. Multimodal sentiment analysis: a survey of methods, trends, and challenges. 2023;55. https:\/\/doi.org\/10.1145\/3586075.","DOI":"10.1145\/3586075"},{"key":"1083_CR22","doi-asserted-by":"publisher","unstructured":"Gandhi A, Adhvaryu K, Poria S, Cambria E, Hussain A. Multimodal sentiment analysis: a systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions. Inf Fusion. 2023;91:424\u2013444. https:\/\/doi.org\/10.1016\/j.inffus.2022.09.025.","DOI":"10.1016\/j.inffus.2022.09.025"},{"key":"1083_CR23","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1007\/s10462-023-10555-8","volume":"56","author":"A Ghorbanali","year":"2023","unstructured":"Ghorbanali A, Sohrabi MK. A comprehensive survey on deep learning-based approaches for multimodal sentiment analysis. Artif Intell Rev. 2023;56:1479\u2013512.","journal-title":"Artif Intell Rev"},{"key":"1083_CR24","doi-asserted-by":"publisher","unstructured":"Zhu L, Zhu Z, Zhang C, Xu Y, Kong X. Multimodal sentiment analysis based on fusion methods: a survey. Inf Fusion. 2023;95, 306\u2013325. . https:\/\/doi.org\/10.1016\/j.inffus.2023.02.028.","DOI":"10.1016\/j.inffus.2023.02.028"},{"key":"1083_CR25","doi-asserted-by":"crossref","unstructured":"Zang C, Wang H, Pei M, Liang W. Discovering the real association: multimodal causal reasoning in video question answering. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023. pp. 19027\u201319036.","DOI":"10.1109\/CVPR52729.2023.01824"},{"key":"1083_CR26","doi-asserted-by":"publisher","unstructured":"Zhang L, Hu A, Zhang J, Hu S, Jin Q. (2023). Mpmqa: Multimodal question answering on product manuals. Proceedings of the AAAI Conference on Artificial Intelligence. 2023; 37: 13958\u201313966. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/26634. https:\/\/doi.org\/10.1609\/aaai.v37i11.26634.","DOI":"10.1609\/aaai.v37i11.26634"},{"key":"1083_CR27","first-page":"23716","volume":"35","author":"J-B Alayrac","year":"2022","unstructured":"Alayrac J-B, Donahue J, Luc P, Miech A, Barr I, Hasson Y, Lenc K, Mensch A, Millican K, Reynolds M, et al. Flamingo: a visual language model for few-shot learning. Adv Neural Inf Process Syst. 2022;35:23716\u201336.","journal-title":"Adv Neural Inf Process Syst"},{"key":"1083_CR28","unstructured":"Li F, Zhang R, Zhang H, Zhang Y, Li B, Li W, Ma Z, Li C. Llava-next-interleave: tackling multi-image, video, and 3d in large multimodal models. 2024. arXiv preprint arXiv:2407.07895."},{"key":"1083_CR29","unstructured":"Liu H, Li C, Wu Q, Lee YJ. Visual instruction tuning. In Thirty-seventh Conference on Neural Information Processing Systems. 2023."},{"key":"1083_CR30","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; 1\u201318.","DOI":"10.1007\/s42001-021-00117-9"},{"key":"1083_CR31","unstructured":"Myers L. What\u2019s the best time to post on twitter? 2021 update. https:\/\/louisem.com\/6624\/best-time-to-post-twitter."},{"key":"1083_CR32","unstructured":"Krippendorff K. Computing krippendorff\u2019s alpha-reliability. 2011. https:\/\/repository.upenn.edu\/asc_papers\/43\/."},{"key":"1083_CR33","doi-asserted-by":"crossref","unstructured":"Darwish K, Stefanov P, Aupetit M, Nakov P. Unsupervised user stance detection on twitter. In Proceedings of the International AAAI Conference on Web and Social Media. vol 14, 2020. pp. 141\u2013152.","DOI":"10.1609\/icwsm.v14i1.7286"},{"key":"1083_CR34","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. 2018. https:\/\/arxiv.org\/abs\/1802.03426.","DOI":"10.21105\/joss.00861"},{"key":"1083_CR35","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.esp.2022.10.006","volume":"70","author":"M-J Luz\u00f3n","year":"2023","unstructured":"Luz\u00f3n M-J. Multimodal practices of research groups in twitter: an analysis of stance and engagement. English Specific Purposes. 2023;70:17\u201332.","journal-title":"English Specific Purposes"},{"key":"1083_CR36","unstructured":"Vicente S, Carreira J, Agapito L, Batista J. Reconstructing pascal voc. In Proceedings of the International IEEE Conference on Computer Vision and Pattern Recognition, 2024. pp. 41\u201348."},{"key":"1083_CR37","doi-asserted-by":"crossref","unstructured":"Lin T.-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL. Microsoft coco: Common objects in context. In Proceeding of the European Conference on Computer Vision, 2024. pp. 740\u2013755.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1083_CR38","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60:84\u201390.","journal-title":"Commun ACM"},{"key":"1083_CR39","doi-asserted-by":"crossref","unstructured":"Hutto C, Gilbert E. Vader: a parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, vol 8, 2014. pp. 216\u2013225.","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"1083_CR40","doi-asserted-by":"publisher","first-page":"33","DOI":"10.3390\/bdcc4040033","volume":"4","author":"T Pano","year":"2020","unstructured":"Pano T, Kashef R. A complete vader-based sentiment analysis of bitcoin (btc) tweets during the era of covid-19. Big Data Cogn Comput. 2020;4:33.","journal-title":"Big Data Cogn Comput"},{"key":"1083_CR41","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1073\/pnas.42.1.43","volume":"42","author":"M Rosenblatt","year":"1956","unstructured":"Rosenblatt M. A central limit theorem and a strong mixing condition. Proc Natl Acad Sci USA. 1956;42:43.","journal-title":"Proc Natl Acad Sci USA"},{"key":"1083_CR42","doi-asserted-by":"crossref","unstructured":"Maharani W, Gozali AA, et\u00a0al. Degree centrality and eigenvector centrality in twitter. In Proceedings of the International Conference on Telecommunication Systems Services and Applications, 2014. pp. 1\u20135.","DOI":"10.1109\/TSSA.2014.7065911"},{"key":"1083_CR43","doi-asserted-by":"crossref","unstructured":"Howlader P, Sudeep K. Degree centrality, eigenvector centrality and the relation between them in twitter. In Proceddings of the IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, 2016. pp. 678\u2013682.","DOI":"10.1109\/RTEICT.2016.7807909"},{"key":"1083_CR44","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/0378-8733(88)90014-7","volume":"10","author":"JM Bolland","year":"1988","unstructured":"Bolland JM. Sorting out centrality: an analysis of the performance of four centrality models in real and simulated networks. Social Netw. 1988;10:233\u201353.","journal-title":"Social Netw"},{"key":"1083_CR45","doi-asserted-by":"publisher","first-page":"16","DOI":"10.17730\/humo.7.3.f4033344851gl053","volume":"7","author":"A Bavelas","year":"1948","unstructured":"Bavelas A. A mathematical model for group structures. Hum Org. 1948;7:16\u201330.","journal-title":"Hum Org"},{"key":"1083_CR46","doi-asserted-by":"crossref","unstructured":"Lutu PEN. Using twitter mentions and a graph database to analyse social network centrality. In Proceedings of the International Conference on Soft Computing & Machine Intelligence, 2019. pp. 155\u2013159.","DOI":"10.1109\/ISCMI47871.2019.9004313"},{"key":"1083_CR47","doi-asserted-by":"crossref","unstructured":"Weitzel L, Quaresma P, de\u00a0Oliveira JPM Measuring node importance on twitter microblogging. In Proceedings of the International Conference on Web Intelligence, Mining and Semantics, 2012. pp. 1\u20137.","DOI":"10.1145\/2254129.2254145"},{"key":"1083_CR48","doi-asserted-by":"crossref","unstructured":"Wei X, Croft WB. Lda-based document models for ad-hoc retrieval. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006. pp. 178\u2013185.","DOI":"10.1145\/1148170.1148204"},{"key":"1083_CR49","first-page":"525","volume":"12","author":"S Yang","year":"2018","unstructured":"Yang S, Zhang H. Text mining of twitter data using a latent dirichlet allocation topic model and sentiment analysis. Int J Comput Inf Eng. 2018;12:525\u20139.","journal-title":"Int J Comput Inf Eng"},{"key":"1083_CR50","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1111\/j.1467-9221.2008.00668.x","volume":"29","author":"DR Carney","year":"2008","unstructured":"Carney DR, Jost JT, Gosling SD, Potter J. The secret lives of liberals and conservatives: Personality profiles, interaction styles, and the things they leave behind. Polit Psychol. 2008;29:807\u201340.","journal-title":"Polit Psychol"},{"key":"1083_CR51","doi-asserted-by":"crossref","unstructured":"Box-Steffensmeier JM, Moses L. Meaningful messaging: Sentiment in elite social media communication with the public on the covid-19 pandemic. Sci Adv. 2021;7:eabg2898.","DOI":"10.1126\/sciadv.abg2898"},{"key":"1083_CR52","unstructured":"Devlin J, Chang M.-W, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. 2018. https:\/\/arxiv.org\/abs\/1810.04805."},{"key":"1083_CR53","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Trans Signal Process. 1997;45:2673\u201381.","journal-title":"IEEE Trans Signal Process"},{"key":"1083_CR54","unstructured":"Beltagy I, Peters ME, Cohan A. Longformer: the long-document transformer. 2020. https:\/\/arxiv.org\/abs\/2004.05150."},{"key":"1083_CR55","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J. node2vec: Scalable feature learning for networks. In Proceedings of the International ACM SIGKDD Conference on Knowledge Discovery and Data mining, 2016. pp. 855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"key":"1083_CR56","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. 2013. https:\/\/arxiv.org\/abs\/1301.3781."},{"key":"1083_CR57","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13707","volume":"41","author":"J Kim","year":"2024","unstructured":"Kim J, Ahn H, Park E. Multi-pop: Enhancing user engagement with content-based multimodal popularity prediction in social media. Expert Syst. 2024;41: e13707.","journal-title":"Expert Syst"},{"key":"1083_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-022-00674-4","volume":"10","author":"E Park","year":"2023","unstructured":"Park E. Crnet: a multimodal deep convolutional neural network for customer revisit prediction. J Big Data. 2023;10:1.","journal-title":"J Big Data"},{"key":"1083_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124553","volume":"255","author":"S Kim","year":"2024","unstructured":"Kim S, Park E. Stad-gcn: Spatial-temporal attention-based dynamic graph convolutional network for retail market price prediction. Expert Syst Appl. 2024;255: 124553.","journal-title":"Expert Syst Appl"},{"key":"1083_CR60","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems, vol 30, 2017. pp. 5998\u20136008."},{"key":"1083_CR61","unstructured":"Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks. In D.\u00a0Precup, & Y.\u00a0W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning, 2017. pp. 3319\u20133328. PMLR volume\u00a070 of Proceedings of Machine Learning Research. https:\/\/proceedings.mlr.press\/v70\/sundararajan17a.html."},{"key":"1083_CR62","doi-asserted-by":"crossref","unstructured":"Johnson K, Lee I-T, Goldwasser D. Ideological phrase indicators for classification of political discourse framing on twitter. In Proceedings of the Second Workshop on NLP and Computational Social Science, 2017. pp. 90\u201399.","DOI":"10.18653\/v1\/W17-2913"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01083-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-025-01083-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-025-01083-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T07:50:31Z","timestamp":1737877831000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-025-01083-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,26]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1083"],"URL":"https:\/\/doi.org\/10.1186\/s40537-025-01083-z","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,26]]},"assertion":[{"value":"16 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from all the participants. Our study received the institutional review board (IRB) approval from the affiliated university (2020-11-025).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"14"}}