{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:57:19Z","timestamp":1776837439877,"version":"3.51.2"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s40747-025-01800-4","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T02:08:38Z","timestamp":1740967718000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1416-5442","authenticated-orcid":false,"given":"Yun","family":"Chu","sequence":"first","affiliation":[]},{"given":"Qiuhao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Enze","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2513-1405","authenticated-orcid":false,"given":"Ling","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"1800_CR1","doi-asserted-by":"crossref","unstructured":"Stolz D, Mkorombindo T, Schumann DM, Agusti A, Ash SY, Bafadhel M, Bai C, Chalmers JD, Criner GJ, Dharmage SC, Franssen FM (2022) Towards the elimination of chronic obstructive pulmonary disease: a lancet commission. Lancet 400(10356):921-972","DOI":"10.1016\/S0140-6736(22)01273-9"},{"issue":"11","key":"1800_CR2","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.0030442","volume":"3","author":"CD Mathers","year":"2006","unstructured":"Mathers CD, Loncar D (2006) Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 3(11):e442","journal-title":"PLoS Med"},{"issue":"1","key":"1800_CR3","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/S2213-2600(19)30418-7","volume":"8","author":"E Harding","year":"2020","unstructured":"Harding E (2020) Who global progress report on tuberculosis elimination. Lancet Respir Med 8(1):19","journal-title":"Lancet Respir Med"},{"key":"1800_CR4","doi-asserted-by":"crossref","unstructured":"Levine SM, Marciniuk DD (2022) Global impact of respiratory disease: what can we do, together, to make a difference?. Chest 161(5):1153-1154","DOI":"10.1016\/j.chest.2022.01.014"},{"issue":"8","key":"1800_CR5","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1056\/NEJMra1302901","volume":"370","author":"A Bohadana","year":"2014","unstructured":"Bohadana A, Izbicki G, Kraman SS (2014) Fundamentals of lung auscultation. N Engl J Med 370(8):744\u2013751","journal-title":"N Engl J Med"},{"issue":"5","key":"1800_CR6","volume":"12","author":"RXA Pramono","year":"2017","unstructured":"Pramono RXA, Bowyer S, Rodriguez-Villegas E (2017) Automatic adventitious respiratory sound analysis: a systematic review. PLoS ONE 12(5):e0177926","journal-title":"PLoS ONE"},{"key":"1800_CR7","first-page":"45","volume":"2","author":"S Reichert","year":"2008","unstructured":"Reichert S, Gass R, Brandt C, Andr\u00e8s E (2008) Analysis of respiratory sounds: state of the art. Clin Med Circul Respirat Pulmon Med 2:45\u201358","journal-title":"Clin Med Circul Respirat Pulmon Med"},{"key":"1800_CR8","doi-asserted-by":"crossref","unstructured":"Bahoura M, Pelletier C (2003) New parameters for respiratory sound classification. In: CCECE 2003-Canadian conference on electrical and computer engineering. toward a caring and humane technology (Cat. No. 03CH37436), vol\u00a03. IEEE, pp 1457\u20131460","DOI":"10.1109\/CCECE.2003.1226178"},{"key":"1800_CR9","doi-asserted-by":"crossref","unstructured":"Gairola S, Tom F, Kwatra N, Jain M (2021) Respirenet: a deep neural network for accurately detecting abnormal lung sounds in limited data setting. In: 2021 43rd Annual international conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, pp\u00a0527\u2013530","DOI":"10.1109\/EMBC46164.2021.9630091"},{"key":"1800_CR10","doi-asserted-by":"crossref","unstructured":"Ma Y, Xu X, Li Y (2020) Lungrn+ nl: an improved adventitious lung sound classification using non-local block resnet neural network with mixup data augmentation. In: Interspeech, pp\u00a02902\u20132906","DOI":"10.21437\/Interspeech.2020-2487"},{"issue":"3","key":"1800_CR11","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.3390\/s22031232","volume":"22","author":"G Petmezas","year":"2022","unstructured":"Petmezas G, Cheimariotis G-A, Stefanopoulos L, Rocha B, Paiva RP, Katsaggelos AK, Maglaveras N (2022) Automated lung sound classification using a hybrid cnn-lstm network and focal loss function. Sensors 22(3):1232","journal-title":"Sensors"},{"key":"1800_CR12","unstructured":"Kim JW, Yoon C, Toikkanen M, Bae S, Jung HY (2023) Adversarial fine-tuning using generated respiratory sound to address class imbalance. arXiv:2311.06480"},{"key":"1800_CR13","doi-asserted-by":"crossref","unstructured":"Kim J-W, Toikkanen M, Bae S, Kim M, Jung H-Y (2024) Repaugment: input-agnostic representation-level augmentation for respiratory sound classification. arXiv e-prints, pp arXiv-2405","DOI":"10.1109\/EMBC53108.2024.10782363"},{"key":"1800_CR14","unstructured":"Chua Y-W, Cheng Y-C (2024) Towards enhanced classification of abnormal lung sound in multi-breath: a light weight multi-label and multi-head attention classification method. arXiv e-prints, pp\u00a0arXiv-2407"},{"issue":"10","key":"1800_CR15","volume":"42","author":"J Li","year":"2021","unstructured":"Li J, Yuan J, Wang H, Liu S, Guo Q, Ma Y, Li Y, Zhao L, Wang G (2021) Lungattn: advanced lung sound classification using attention mechanism with dual tqwt and triple stft spectrogram. Physiol Meas 42(10):105006","journal-title":"Physiol Meas"},{"issue":"7","key":"1800_CR16","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1109\/JBHI.2020.3048006","volume":"25","author":"SB Shuvo","year":"2020","unstructured":"Shuvo SB, Ali SN, Swapnil SI, Hasan T, Bhuiyan MIH (2020) A lightweight cnn model for detecting respiratory diseases from lung auscultation sounds using emd-cwt-based hybrid scalogram. IEEE J Biomed Health Inform 25(7):2595\u20132603","journal-title":"IEEE J Biomed Health Inform"},{"key":"1800_CR17","doi-asserted-by":"crossref","unstructured":"Gupta S, Agrawal M, Deepak D (2022) Classification of auscultation sounds into objective spirometry findings using mvmd and 3d cnn. In: 2022 National conference on communications (NCC). IEEE, pp\u00a042\u201347","DOI":"10.1109\/NCC55593.2022.9806737"},{"key":"1800_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3428637","author":"F Wang","year":"2024","unstructured":"Wang F, Yuan X, Bao J, Lam C-T, Huang G, Chen H (2024) Ofgst-swin: swin transformer utilizing overlap fusion-based generalized s-transform for respiratory cycle classification. IEEE Trans Instrum Meas. https:\/\/doi.org\/10.1109\/TIM.2024.3428637","journal-title":"IEEE Trans Instrum Meas"},{"key":"1800_CR19","doi-asserted-by":"crossref","first-page":"32845","DOI":"10.1109\/ACCESS.2019.2903859","volume":"7","author":"H Chen","year":"2019","unstructured":"Chen H, Yuan X, Pei Z, Li M, Li J (2019) Triple-classification of respiratory sounds using optimized s-transform and deep residual networks. IEEE Access 7:32845\u201332852","journal-title":"IEEE Access"},{"key":"1800_CR20","doi-asserted-by":"crossref","unstructured":"Minami K, Lu H, Kim H, Mabu S, Hirano Y, Kido S (2019) Automatic classification of large-scale respiratory sound dataset based on convolutional neural network. In: 2019 19th International conference on control, automation and systems (ICCAS). IEEE, pp\u00a0804\u2013807","DOI":"10.23919\/ICCAS47443.2019.8971689"},{"key":"1800_CR21","doi-asserted-by":"crossref","unstructured":"Ma Y, Xu X, Yu Q, Zhang Y, Li Y, Zhao J, and Wang G (2019) Lungbrn: A smart digital stethoscope for detecting respiratory disease using bi-resnet deep learning algorithm. In: 2019 IEEE biomedical circuits and systems conference (BioCAS). IEEE, pp\u00a01\u20134","DOI":"10.1109\/BIOCAS.2019.8919021"},{"key":"1800_CR22","doi-asserted-by":"crossref","unstructured":"Xu L, Cheng J, Liu J, Kuang H, Wu F, Wang J (2021) Arsc-net: adventitious respiratory sound classification network using parallel paths with channel-spatial attention. In: 2021 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp\u00a01125\u20131130","DOI":"10.1109\/BIBM52615.2021.9669787"},{"key":"1800_CR23","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.artmed.2018.04.008","volume":"88","author":"D Bardou","year":"2018","unstructured":"Bardou D, Zhang K, Ahmad SM (2018) Lung sounds classification using convolutional neural networks. Artif Intell Med 88:58\u201369","journal-title":"Artif Intell Med"},{"key":"1800_CR24","volume":"70","author":"S Gupta","year":"2021","unstructured":"Gupta S, Agrawal M, Deepak D (2021) Gammatonegram based triple classification of lung sounds using deep convolutional neural network with transfer learning. Biomed Signal Process Control 70:102947","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"1800_CR25","doi-asserted-by":"crossref","first-page":"682","DOI":"10.3390\/s24020682","volume":"24","author":"LD Mang","year":"2024","unstructured":"Mang LD, Gonz\u00e1lez Mart\u00ednez FD, Martinez Mu\u00f1oz D, Garc\u00eda Gal\u00e1n S, Cortina R (2024) Classification of adventitious sounds combining cochleogram and vision transformers. Sensors 24(2):682","journal-title":"Sensors"},{"key":"1800_CR26","doi-asserted-by":"crossref","unstructured":"Li S, Liu Y (2010) Feature extraction of lung sounds based on bispectrum analysis. In: 2010 Third international symposium on information processing. IEEE, pp\u00a0393\u2013397","DOI":"10.1109\/ISIP.2010.136"},{"key":"1800_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-020-01681-9","volume":"45","author":"H Mukherjee","year":"2021","unstructured":"Mukherjee H, Sreerama P, Dhar A, Obaidullah SM, Roy K, Mahmud M, Santosh K (2021) Automatic lung health screening using respiratory sounds. J Med Syst 45:1\u20139","journal-title":"J Med Syst"},{"issue":"1","key":"1800_CR28","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s13755-019-0091-3","volume":"8","author":"F Demir","year":"2019","unstructured":"Demir F, Sengur A, Bajaj V (2019) Convolutional neural networks based efficient approach for classification of lung diseases. Health Inf Sci Syst 8(1):4","journal-title":"Health Inf Sci Syst"},{"key":"1800_CR29","doi-asserted-by":"publisher","unstructured":"Niizumi D, Takeuchi D, Ohishi Y, Harada N, Kashino K (2024) Masked modeling duo: towards a universal audio pre-training framework. IEEE\/ACM Trans Audio Speech Lang Process 32:2391-2406. https:\/\/doi.org\/10.1109\/TASLP.2024.3389636","DOI":"10.1109\/TASLP.2024.3389636"},{"key":"1800_CR30","doi-asserted-by":"publisher","unstructured":"Bae S, Kim JW, Cho WY, Baek H, Son S, Lee B, Ha C, Tae K, Kim S, Yun SY (2023) Patch-mix contrastive learning with audio spectrogram transformer on respiratory sound classification. In: Interspeech 2023, pp 5436-5440. https:\/\/doi.org\/10.21437\/Interspeech.2023-1426 (ISSN: 2958-1796)","DOI":"10.21437\/Interspeech.2023-1426"},{"key":"1800_CR31","doi-asserted-by":"crossref","unstructured":"Cai H, Li J, Hu M, Gan C, Han S (2023) Efficientvit: lightweight multi-scale attention for high-resolution dense prediction. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp\u00a017302\u201317313","DOI":"10.1109\/ICCV51070.2023.01587"},{"issue":"1","key":"1800_CR32","first-page":"44","volume":"10","author":"D-M Huang","year":"2023","unstructured":"Huang D-M, Huang J, Qiao K, Zhong N-S, Lu H-Z, Wang W-J (2023) Deep learning-based lung sound analysis for intelligent stethoscope. Mil Med Res 10(1):44","journal-title":"Mil Med Res"},{"key":"1800_CR33","volume":"80","author":"W Song","year":"2023","unstructured":"Song W, Han J (2023) Patch-level contrastive embedding learning for respiratory sound classification. Biomed Signal Process Control 80:104338","journal-title":"Biomed Signal Process Control"},{"key":"1800_CR34","doi-asserted-by":"crossref","unstructured":"Song W, Han J, Song H (2021) Contrastive embedding learning method for respiratory sound classification. In: ICASSP 2021\u20132021 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp\u00a01275\u20131279","DOI":"10.1109\/ICASSP39728.2021.9414385"},{"key":"1800_CR35","doi-asserted-by":"crossref","unstructured":"Moummad I, Farrugia N (2023) Pretraining respiratory sound representations using metadata and contrastive learning. In: 2023 IEEE workshop on applications of signal processing to audio and acoustics (WASPAA). IEEE, pp\u00a01\u20135","DOI":"10.1109\/WASPAA58266.2023.10248130"},{"key":"1800_CR36","doi-asserted-by":"crossref","unstructured":"Kim J-W, Bae S, Cho W-Y, Lee B, Jung H-Y (2024) Stethoscope-guided supervised contrastive learning for cross-domain adaptation on respiratory sound classification. In: ICASSP 2024\u20132024 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp\u00a01431\u20131435","DOI":"10.1109\/ICASSP48485.2024.10447734"},{"key":"1800_CR37","unstructured":"Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: International conference on machine learning. PMLR, pp\u00a0478\u2013487"},{"key":"1800_CR38","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.patrec.2020.07.028","volume":"138","author":"MM Fard","year":"2020","unstructured":"Fard MM, Thonet T, Gaussier E (2020) Deep k-means: jointly clustering with k-means and learning representations. Pattern Recogn Lett 138:185\u2013192","journal-title":"Pattern Recogn Lett"},{"key":"1800_CR39","doi-asserted-by":"crossref","unstructured":"Yang J, Parikh D, Batra D (2016) Joint unsupervised learning of deep representations and image clusters. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp\u00a05147\u20135156","DOI":"10.1109\/CVPR.2016.556"},{"key":"1800_CR40","doi-asserted-by":"crossref","unstructured":"Chang J, Wang L, Meng G, Xiang S, Pan C (2017) Deep adaptive image clustering. In: Proceedings of the IEEE international conference on computer vision, pp\u00a05879\u20135887","DOI":"10.1109\/ICCV.2017.626"},{"issue":"9","key":"1800_CR41","first-page":"1680","volume":"32","author":"X Guo","year":"2019","unstructured":"Guo X, Liu X, Zhu E, Zhu X, Li M, Xu X, Yin J (2019) Adaptive self-paced deep clustering with data augmentation. IEEE Trans Knowl Data Eng 32(9):1680\u20131693","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1800_CR42","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1007\/s10618-020-00734-4","volume":"35","author":"H Zhang","year":"2021","unstructured":"Zhang H, Zhan T, Basu S, Davidson I (2021) A framework for deep constrained clustering. Data Min Knowl Discov 35:593\u2013620","journal-title":"Data Min Knowl Discov"},{"key":"1800_CR43","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Almagro G, Suarez JL, Luengo J, Cano J-R, Garc\u00eda S (2020) Agglomerative constrained clustering through similarity and distance recalculation. In: International conference on hybrid artificial intelligence systems, pp\u00a0424\u2013436","DOI":"10.1007\/978-3-030-61705-9_35"},{"key":"1800_CR44","doi-asserted-by":"crossref","unstructured":"Wang Z, Wang Z (2022) A domain transfer based data augmentation method for automated respiratory classification. In: ICASSP 2022\u20132022 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp\u00a09017\u20139021","DOI":"10.1109\/ICASSP43922.2022.9746941"},{"key":"1800_CR45","doi-asserted-by":"crossref","unstructured":"Pham L, Ngo D, Tran K, Hoang T, Schindler A, McLoughlin I (2022) An ensemble of deep learning frameworks for predicting respiratory anomalies. In: Annual international conference of the IEEE engineering in medicine and biology society. Annual international conference, vol\u00a02022. IEEE Engineering in Medicine and Biology Society, pp\u00a04595\u20134598","DOI":"10.1109\/EMBC48229.2022.9871440"},{"issue":"9","key":"1800_CR46","doi-asserted-by":"crossref","first-page":"2872","DOI":"10.1109\/TBME.2022.3156293","volume":"69","author":"T Nguyen","year":"2022","unstructured":"Nguyen T, Pernkopf F (2022) Lung sound classification using co-tuning and stochastic normalization. IEEE Trans Biomed Eng 69(9):2872\u20132882","journal-title":"IEEE Trans Biomed Eng"},{"key":"1800_CR47","doi-asserted-by":"publisher","unstructured":"Yang Z, Liu S, Song M, Parada-Cabaleiro E, Schuller BW (2020) Adventitious respiratory classification using attentive residual neural networks. In: Interspeech 2020, pp 2912-2916. https:\/\/doi.org\/10.21437\/Interspeech.2020-2790 (ISSN: 2958-1796)","DOI":"10.21437\/Interspeech.2020-2790"},{"issue":"8","key":"1800_CR48","doi-asserted-by":"crossref","first-page":"2938","DOI":"10.1109\/JBHI.2021.3064237","volume":"25","author":"L Pham","year":"2021","unstructured":"Pham L, Phan H, Palaniappan R, Mertins A, McLoughlin I (2021) Cnn-moe based framework for classification of respiratory anomalies and lung disease detection. IEEE J Biomed Health Inform 25(8):2938\u20132947","journal-title":"IEEE J Biomed Health Inform"},{"key":"1800_CR49","doi-asserted-by":"crossref","unstructured":"Ren Z, Nguyen TT, Nejdl W (2022) Prototype learning for interpretable respiratory sound analysis. In: ICASSP 2022\u20132022 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp\u00a09087\u20139091","DOI":"10.1109\/ICASSP43922.2022.9747014"},{"key":"1800_CR50","doi-asserted-by":"publisher","unstructured":"Chang Y, Ren Z, Nguyen TT, Nejdl W, Schuller BW (2022) Example-based explanations with adversarial attacks for respiratory sound analysis. In: Interspeech 2022, pp 4003-4007. https:\/\/doi.org\/10.21437\/Interspeech.2022-11355 (ISSN: 2958-1796)","DOI":"10.21437\/Interspeech.2022-11355"},{"issue":"3","key":"1800_CR51","first-page":"535","volume":"14","author":"J Acharya","year":"2020","unstructured":"Acharya J, Basu A (2020) Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning. IEEE Trans Biomed Circuits Syst 14(3):535\u2013544","journal-title":"IEEE Trans Biomed Circuits Syst"},{"key":"1800_CR52","doi-asserted-by":"crossref","unstructured":"Kochetov K, Putin E, Balashov M, Filchenkov A, Shalyto A (2018) Noise masking recurrent neural network for respiratory sound classification. In: Artificial neural networks and machine learning\u2013ICANN 2018: 27th international conference on artificial neural networks, Rhodes, Greece, October 4\u20137, 2018, proceedings, Part III 27. Springer, pp\u00a0208\u2013217","DOI":"10.1007\/978-3-030-01424-7_21"},{"key":"1800_CR53","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2022.104078","volume":"130","author":"Z Chen","year":"2022","unstructured":"Chen Z, Li M, Wang R, Sun W, Liu J, Li H, Wang T, Lian Y, Zhang J, Wang X (2022) Diagnosis of covid-19 via acoustic analysis and artificial intelligence by monitoring breath sounds on smartphones. J Biomed Inform 130:104078","journal-title":"J Biomed Inform"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01800-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01800-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01800-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T21:23:19Z","timestamp":1743369799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01800-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,3]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1800"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01800-4","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,3]]},"assertion":[{"value":"4 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 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":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This work adheres to the guidelines and ethical considerations outlined in the privacy policies of each social media platform involved in our study. We acknowledge the public nature and free accessibility of content posted on social network platforms, which are not password-protected and have thousands of active users. All analyses are conducted using publicly available data, and we do not attempt to track users across different platforms. In data preprocessing, we anonymized all user names or account IDs. Our data sets do not contain any information about individuals, and we have taken measures to ensure that our results do not disclose the identity of any specific account.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"200"}}