{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:29:32Z","timestamp":1743035372557,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031417733"},{"type":"electronic","value":"9783031417740"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-41774-0_1","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T03:25:20Z","timestamp":1695266720000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing the\u00a0Effects of\u00a0Expanded Input Elicitation and\u00a0Machine Learning-Based Priming on\u00a0Crowd Stock Prediction"],"prefix":"10.1007","author":[{"given":"Harika","family":"Bhogaraju","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arushi","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jyotika","family":"Jaiswal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4843-3564","authenticated-orcid":false,"given":"Adolfo R.","family":"Escobedo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"1_CR1","unstructured":"Machine learning approaches in stock price prediction: A systematic review. https:\/\/iopscience.iop.org\/article\/10.1088\/1742-6596\/2161\/1\/012065"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Bassamboo, A., Cui, R., Moreno, A.: Wisdom of crowds in operations: Forecasting using prediction markets (2015). Available at SSRN 2679663","DOI":"10.2139\/ssrn.2679663"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Checkley, M.S., Hig\u00f3n, D.A., Alles, H.: The hasty wisdom of the mob: How market sentiment predicts stock market behavior. Expert Syst. Appl. 77, 256-263 (2017). https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0957417417300398","DOI":"10.1016\/j.eswa.2017.01.029"},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/s13278-022-00919-3","volume":"12","author":"N Das","year":"2022","unstructured":"Das, N., Sadhukhan, B., Chatterjee, T., Chakrabarti, S.: Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak. Soc. Netw. Anal. Min. 12(1), 92 (2022)","journal-title":"Soc. Netw. Anal. Min."},{"issue":"3","key":"1_CR5","first-page":"65","volume":"43","author":"G Demartini","year":"2020","unstructured":"Demartini, G., Mizzaro, S., Spina, D.: Human-in-the-loop artificial intelligence for fighting online misinformation: Challenges and opportunities. IEEE Data Eng. Bull. 43(3), 65\u201374 (2020)","journal-title":"IEEE Data Eng. Bull."},{"issue":"3","key":"1_CR6","doi-asserted-by":"publisher","first-page":"11","DOI":"10.14254\/2071-789X.2018\/11-3\/1","volume":"11","author":"T Endress","year":"2018","unstructured":"Endress, T., et al.: deliberated intuition in stock price forecasting. Econ. Sociol. 11(3), 11\u201327 (2018)","journal-title":"Econ. Sociol."},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"105354","DOI":"10.1016\/j.cmpb.2020.105354","volume":"190","author":"S Fathi","year":"2020","unstructured":"Fathi, S., Ahmadi, M., Birashk, B., Dehnad, A.: Development and use of a clinical decision support system for the diagnosis of social anxiety disorder. Comput. Methods Programs Biomed. 190, 105354 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"1_CR8","doi-asserted-by":"publisher","first-page":"73","DOI":"10.2753\/JEC1086-4415150304","volume":"15","author":"S Hill","year":"2011","unstructured":"Hill, S., Ready-Campbell, N.: Expert stock picker: the wisdom of (experts in) crowds. Int. J. Electron. Commer. 15(3), 73\u2013102 (2011)","journal-title":"Int. J. Electron. Commer."},{"issue":"1","key":"1_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-11900-7","volume":"12","author":"H Honda","year":"2022","unstructured":"Honda, H., Kagawa, R., Shirasuna, M.: On the round number bias and wisdom of crowds in different response formats for numerical estimation. Sci. Rep. 12(1), 1\u201318 (2022)","journal-title":"Sci. Rep."},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Huang, Y., Capretz, L.F., Ho, D.: Machine learning for stock prediction based on fundamental analysis. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 01\u201310. IEEE (2021)","DOI":"10.1109\/SSCI50451.2021.9660134"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Martins, C.J.L., et al.: Information diffusion, trading speed and their potential impact on price efficiency-Literature review. Borsa Istanbul Rev. 22(1), 122-132 (2021). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2214845021000193","DOI":"10.1016\/j.bir.2021.02.006"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725\u20131732 (2014)","DOI":"10.1109\/CVPR.2014.223"},{"key":"1_CR13","unstructured":"Kemmer, R., Yoo, Y., Escobedo, A., Maciejewski, R.: Enhancing collective estimates by aggregating cardinal and ordinal inputs. https:\/\/ojs.aaai.org\/index.php\/HCOMP\/article\/view\/7465"},{"key":"1_CR14","series-title":"Lecture Notes on Data Engineering and Communications Technologies","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/978-3-030-37309-2_10","volume-title":"Data Science: From Research to Application","author":"A Khodabakhsh","year":"2020","unstructured":"Khodabakhsh, A., Ari, I., Bak\u0131r, M., Alagoz, S.M.: Forecasting multivariate time-series data using LSTM and mini-batches. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds.) CiDaS 2019. LNDECT, vol. 45, pp. 121\u2013129. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37309-2_10"},{"key":"1_CR15","unstructured":"links open overlay panelAdil Moghar a, A., a, b, has never been easy to invest in a set of assets, A.: Stock market prediction using lstm recurrent neural network (2020). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050920304865"},{"key":"1_CR16","unstructured":"Mohanty, S., Vijay, A., Gopakumar, N.: Stockbot: Using lstms to predict stock prices. arXiv preprint arXiv:2207.06605 (2022)"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Mojjada, R.K., Yadav, A., Prabhu, A., Natarajan, Y.: Machine learning models for COVID-19 future forecasting. Materials Today: Proceedings (2020)","DOI":"10.1016\/j.matpr.2020.10.962"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Q., Xu, W., Zheng, H.: Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles. Neurocomputing, 299, 51-61 (2018). https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0925231218303540","DOI":"10.1016\/j.neucom.2018.02.095"},{"key":"1_CR19","doi-asserted-by":"publisher","first-page":"4773","DOI":"10.1080\/00207543.2021.1956675","volume":"59","author":"R Rai","year":"2021","unstructured":"Rai, R., Tiwari, M.K., Ivanov, D., Dolgui, A.: Machine learning in manufacturing and industry 4.0 applications. Int. J. Prod. Res. 59, 4773\u20134778 (2021)","journal-title":"Int. J. Prod. Res."},{"key":"1_CR20","unstructured":"Remias, R.: President Trump\u2019s Tweets and their Effect on the Stock Market: The Relationship Between Social Media, Politics, and Emotional Economic Decision-Making. Ph.D. thesis, Wittenberg University (2021)"},{"issue":"15","key":"1_CR21","first-page":"369","volume":"119","author":"R Seethalakshmi","year":"2018","unstructured":"Seethalakshmi, R.: Analysis of stock market predictor variables using linear regression. Int. J. Pure and Appl. Math. 119(15), 369\u2013378 (2018)","journal-title":"Int. J. Pure and Appl. Math."},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Shang, S., Hui, P., Kulkarni, S.R., Cuff, P.W.: Wisdom of the crowd: incorporating social influence in recommendation models. In: 2011 IEEE 17th International Conference on Parallel and Distributed Systems, pp. 835\u2013840. IEEE (2011)","DOI":"10.1109\/ICPADS.2011.150"},{"issue":"1","key":"1_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00333-6","volume":"7","author":"J Shen","year":"2020","unstructured":"Shen, J., Shafiq, M.O.: Short-term stock market price trend prediction using a comprehensive deep learning system. J. big Data 7(1), 1\u201333 (2020)","journal-title":"J. big Data"},{"key":"1_CR24","unstructured":"Siah, K.W., Myers, P.: Stock market prediction through technical and public sentiment analysis (2016)"},{"key":"1_CR25","unstructured":"Surowiecki, J.: The wisdom of crowds. Anchor (2005)"},{"key":"1_CR26","unstructured":"Team, K.: Keras documentation: Adam. https:\/\/keras.io\/api\/optimizers\/adam\/#: :text=Adam"},{"issue":"1","key":"1_CR27","doi-asserted-by":"publisher","first-page":"010502","DOI":"10.7189\/jogh.08.010502","volume":"8","author":"K Wazny","year":"2018","unstructured":"Wazny, K.: Applications of crowdsourcing in health: an overview. J. Global Health 8(1), 010502 (2018)","journal-title":"J. Global Health"},{"key":"1_CR28","unstructured":"Yasmin, R., Grassel, J.T., Hassan, M.M., Fuentes, O., Escobedo, A.R.: Enhancing image classification capabilities of crowdsourcing-based methods through expanded input elicitation. https:\/\/ojs.aaai.org\/index.php\/HCOMP\/article\/view\/18949"},{"key":"1_CR29","doi-asserted-by":"publisher","first-page":"848056","DOI":"10.3389\/frai.2022.848056","volume":"5","author":"R Yasmin","year":"2022","unstructured":"Yasmin, R., Hassan, M.M., Grassel, J.T., Bhogaraju, H., Escobedo, A.R., Fuentes, O.: Improving crowdsourcing-based image classification through expanded input elicitation and machine learning. Front. Artif. Intell. 5, 848056 (2022)","journal-title":"Front. Artif. Intell."}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41774-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:30:20Z","timestamp":1695277820000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41774-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031417733","9783031417740"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41774-0_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Budapest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hungary","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"218","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.01","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.86","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}