{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:28:29Z","timestamp":1773808109659,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s11042-020-09873-8","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T11:02:54Z","timestamp":1601031774000},"page":"3927-3949","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["A stacked convolutional neural network for detecting the resource tweets during a disaster"],"prefix":"10.1007","volume":"80","author":[{"given":"Sreenivasulu","family":"Madichetty","sequence":"first","affiliation":[]},{"given":"Sridevi","family":"M.","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,25]]},"reference":[{"key":"9873_CR1","doi-asserted-by":"crossref","unstructured":"Basu M, Ghosh K, Das S, Dey R, Bandyopadhyay S, Ghosh S (2017) Identifying post-disaster resource needs and availabilities from microblogs. In: Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. ACM, pp 427\u2013430","DOI":"10.1145\/3110025.3110036"},{"key":"9873_CR2","doi-asserted-by":"crossref","unstructured":"Basu M, Ghosh S, Jana A, Bandyopadhyay S, Singh R (2017) Resource mapping during a natural disaster: A case study on the 2015 Nepal earthquake. International Journal of Disaster Risk Reduction","DOI":"10.1016\/j.ijdrr.2017.05.020"},{"issue":"3","key":"9873_CR3","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1109\/TCSS.2019.2914179","volume":"6","author":"M Basu","year":"2019","unstructured":"Basu M, Shandilya A, Khosla P, Ghosh K, Ghosh S (2019) Extracting resource needs and availabilities from microblogs for aiding post-disaster relief operations. IEEE Trans Comput Soc Syst 6(3):604\u2013618","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"Aug","key":"9873_CR4","first-page":"2493","volume":"12","author":"R Collobert","year":"2011","unstructured":"Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12(Aug):2493\u20132537","journal-title":"J Mach Learn Res"},{"issue":"7","key":"9873_CR5","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1162\/089976698300017197","volume":"10","author":"TG Dietterich","year":"1998","unstructured":"Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10(7):1895\u20131923","journal-title":"Neural Comput"},{"issue":"5","key":"9873_CR6","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1016\/j.ipm.2019.05.010","volume":"56","author":"R Dutt","year":"2019","unstructured":"Dutt R, Basu M, Ghosh K, Ghosh S (2019) Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities. Inf Process Manag 56(5):1680\u20131697","journal-title":"Inf Process Manag"},{"key":"9873_CR7","doi-asserted-by":"crossref","unstructured":"Ganguly D, Ghosh K (2018) Contextual word embedding: a case study in clustering tweets about emergency situations. In: Companion of the the web conference 2018 on the web conference 2018. International world wide web conferences steering committee, pp 73\u201374","DOI":"10.1145\/3184558.3186935"},{"key":"9873_CR8","doi-asserted-by":"crossref","unstructured":"Gupta H, Jamal MS, Madisetty S, Desarkar MS (2018) A framework for real-time spam detection in twitter. In: 2018 10Th international conference on communication systems & networks (COMSNETS). IEEE, pp 380\u2013383","DOI":"10.1109\/COMSNETS.2018.8328222"},{"key":"9873_CR9","doi-asserted-by":"crossref","unstructured":"Imran M, Castillo C, Ji L, Meier P, Vieweg S (2014) Aidr: Artificial intelligence for disaster response. In: Proceedings of the 23rd International Conference on World Wide Web. ACM, pp 159\u2013162","DOI":"10.1145\/2567948.2577034"},{"issue":"4","key":"9873_CR10","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1145\/2771588","volume":"47","author":"M Imran","year":"2015","unstructured":"Imran M, Castillo C, Diaz F, Vieweg S (2015) Processing social media messages in mass emergency A survey. ACM Comput Surv (CSUR) 47 (4):67","journal-title":"ACM Comput Surv (CSUR)"},{"key":"9873_CR11","unstructured":"Khosla P, Basu M, Ghosh K, Ghosh S (2017) Microblog retrieval for post-disaster relief: Applying and comparing neural ir models. arXiv:1707.06112"},{"key":"9873_CR12","doi-asserted-by":"crossref","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. arXiv:1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"issue":"2","key":"9873_CR13","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1109\/TCSS.2020.2980007","volume":"7","author":"L Li","year":"2020","unstructured":"Li L, Zhang Q, Wang X, Zhang J, Wang T, Gao T-L, Duan W, Tsoi KK-f, Wang F-y (2020) Characterizing the propagation of situational information in social media during covid-19 epidemic A case study on weibo. IEEE Trans Comput Soc Syst 7(2):556\u2013562","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"9873_CR14","doi-asserted-by":"crossref","unstructured":"Madichetty S, Sridevi M (2019) Detecting informative tweets during disaster using deep neural networks. In: 2019 11Th international conference on communication systems & networks (COMSNETS). IEEE, pp 709\u2013713","DOI":"10.1109\/COMSNETS.2019.8711095"},{"key":"9873_CR15","doi-asserted-by":"crossref","unstructured":"Madisetty S, Desarkar MS (2017) Identification of relevant hashtags for planned events using learning to rank. In: International joint conference on knowledge discovery, knowledge engineering, and knowledge management. Springer, pp 82\u201399","DOI":"10.1007\/978-3-030-15640-4_5"},{"key":"9873_CR16","doi-asserted-by":"crossref","unstructured":"Madisetty S, Desarkar MS (2017) An ensemble based method for predicting emotion intensity of tweets. In: International conference on mining intelligence and knowledge exploration. Springer, pp 359\u2013370","DOI":"10.1007\/978-3-319-71928-3_34"},{"key":"9873_CR17","doi-asserted-by":"crossref","unstructured":"Madisetty S, Desarkar MS (2017) Nsemo at emoint-2017: an ensemble to predict emotion intensity in tweets. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp 219\u2013224","DOI":"10.18653\/v1\/W17-5230"},{"key":"9873_CR18","doi-asserted-by":"crossref","unstructured":"Madisetty S, Desarkar MS (2017) Exploiting meta attributes for identifying event related hashtags","DOI":"10.5220\/0006502602380245"},{"issue":"4","key":"9873_CR19","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1109\/TCSS.2018.2878852","volume":"5","author":"S Madisetty","year":"2018","unstructured":"Madisetty S, Desarkar MS (2018) A neural network-based ensemble approach for spam detection in twitter. IEEE Trans Comput Soc Syst 5(4):973\u2013984","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"6","key":"9873_CR20","doi-asserted-by":"publisher","first-page":"430","DOI":"10.3844\/jcssp.2007.430.435","volume":"3","author":"AMA Mesleh","year":"2007","unstructured":"Mesleh AMA (2007) Chi square feature extraction based svms arabic language text categorization system. J Comput Sci 3(6):430\u2013435","journal-title":"J Comput Sci"},{"key":"9873_CR21","doi-asserted-by":"crossref","unstructured":"Nazer TH, Morstatter F, Dani H, Liu H (2016) Finding requests in social media for disaster relief. In: 2016 IEEE\/ACM international conference on Advances in social networks analysis and mining (ASONAM). IEEE, pp 1410\u20131413","DOI":"10.1109\/ASONAM.2016.7752432"},{"key":"9873_CR22","doi-asserted-by":"crossref","unstructured":"Nguyen DT, Mannai KAA, Shafiq Joty S, Sajjad H, Imran M, Mitra P (2016) Rapid classification of crisis-related data on social networks using convolutional neural networks. arXiv:1608.03902","DOI":"10.1609\/icwsm.v11i1.14950"},{"key":"9873_CR23","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"1","key":"9873_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-020-0633-3","volume":"10","author":"H Purohit","year":"2020","unstructured":"Purohit H, Castillo C, Pandey R (2020) Ranking and grouping social media requests for emergency services using serviceability model. Soc Netw Anal Min 10(1):1\u201317","journal-title":"Soc Netw Anal Min"},{"issue":"1","key":"9873_CR25","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3404820.3404823","volume":"12","author":"U Qazi","year":"2020","unstructured":"Qazi U, Imran M, Ofli F (2020) Geocov19: a dataset of hundreds of millions of multilingual covid-19 tweets with location information. SIGSPATIAL Spec 12(1):6\u201315","journal-title":"SIGSPATIAL Spec"},{"key":"9873_CR26","doi-asserted-by":"crossref","unstructured":"Rajdev M, Lee K (2015) Fake and spam messages: Detecting misinformation during natural disasters on social media. In: 2015 IEEE\/WIC\/ACM International conference on web intelligence and intelligent agent technology (WI-IAT), vol 1. IEEE, pp 17\u201320","DOI":"10.1109\/WI-IAT.2015.102"},{"key":"9873_CR27","doi-asserted-by":"crossref","unstructured":"Rios A, Kavuluru R (2015) Convolutional neural networks for biomedical text classification: application in indexing biomedical articles. In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics. ACM, pp 258\u2013267","DOI":"10.1145\/2808719.2808746"},{"key":"9873_CR28","doi-asserted-by":"crossref","unstructured":"Rudra K, Ghosh S, Ganguly N, Goyal P, Ghosh S (2015) Extracting situational information from microblogs during disaster events: a classification-summarization approach. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, pp 583\u2013592","DOI":"10.1145\/2806416.2806485"},{"key":"9873_CR29","doi-asserted-by":"crossref","unstructured":"Rudra K, Sharma A, Ganguly N, Ghosh S (2016) Characterizing communal microblogs during disaster events. In: 2016 IEEE\/ACM international conference on Advances in social networks analysis and mining (ASONAM). IEEE, pp 96\u201399","DOI":"10.1109\/ASONAM.2016.7752219"},{"issue":"3","key":"9873_CR30","first-page":"17","volume":"12","author":"K Rudra","year":"2018","unstructured":"Rudra K, Ganguly N, Goyal P, Ghosh S (2018) Extracting and summarizing situational information from the twitter social media during disasters. ACM Trans Web (TWEB) 12(3):17","journal-title":"ACM Trans Web (TWEB)"},{"issue":"2","key":"9873_CR31","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1109\/TCSS.2018.2802942","volume":"5","author":"K Rudra","year":"2018","unstructured":"Rudra K, Sharma A, Ganguly N, Ghosh S (2018) Characterizing and countering communal microblogs during disaster events. IEEE Trans Comput Soc Syst 5(2):403\u2013417","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"4","key":"9873_CR32","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1109\/TKDE.2012.29","volume":"25","author":"T Sakaki","year":"2013","unstructured":"Sakaki T, Okazaki M, Matsuo Y (2013) Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans Knowl Data Eng 25(4):919\u2013931","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9873_CR33","doi-asserted-by":"crossref","unstructured":"Sarkar A, Roy S, Basu M (2019) Curating resource needs and availabilities from microblog during a natural disaster: A case study on the 2015 chennai floods. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp 338\u2013341","DOI":"10.1145\/3297001.3297055"},{"key":"9873_CR34","unstructured":"Socher R, Huval B, Bath B, Manning CD, Ng AY (2012) Convolutional-recursive deep learning for 3d object classification. In: Advances in neural information processing systems, pp 656\u2013664"},{"key":"9873_CR35","doi-asserted-by":"crossref","unstructured":"Sreenivasulu M, Sridevi M (2017) Mining informative words from the tweets for detecting the resources during disaster. In: International conference on mining intelligence and knowledge exploration. Springer, pp 348\u2013358","DOI":"10.1007\/978-3-319-71928-3_33"},{"key":"9873_CR36","doi-asserted-by":"crossref","unstructured":"Sreenivasulu M, Sridevi M (2018) A survey on event detection methods on various social media. In: Recent findings in intelligent computing techniques. Springer, pp 87\u201393","DOI":"10.1007\/978-981-10-8633-5_9"},{"issue":"1","key":"9873_CR37","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"9873_CR38","doi-asserted-by":"crossref","unstructured":"Torkildson MK, Starbird K, Aragon C (2014) Analysis and visualization of sentiment and emotion on crisis tweets. In: International conference on cooperative design, visualization and engineering. Springer, pp 64\u201367","DOI":"10.1007\/978-3-319-10831-5_9"},{"key":"9873_CR39","unstructured":"Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 1. Wiley, New York"},{"key":"9873_CR40","unstructured":"Varga I, Sano M, Torisawa K, Hashimoto C, Ohtake K, Kawai T, Jong-Hoon O, De Saeger S (2013) Aid is out there Looking for help from tweets during a large scale disaster. In: ACL (1), pp 1619\u20131629"},{"key":"9873_CR41","doi-asserted-by":"crossref","unstructured":"Verma S, Vieweg S, Corvey WJ, Palen L, Martin JH, Palmer M, Schram A, Anderson KM (2011) Natural language processing to the rescue? extracting\u201d situational awareness\u201d tweets during mass emergency. Citeseer, pp 385\u2013392","DOI":"10.1609\/icwsm.v5i1.14119"},{"issue":"1","key":"9873_CR42","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.eswa.2010.06.048","volume":"38","author":"G Wang","year":"2011","unstructured":"Wang G, Hao J, Ma J, Jiang H (2011) A comparative assessment of ensemble learning for credit scoring. Expert Syst Appl 38(1):223\u2013230","journal-title":"Expert Syst Appl"},{"key":"9873_CR43","doi-asserted-by":"crossref","unstructured":"Wang S, Chen Z, Liu B, Emery S (2016) Identifying search keywords for finding relevant social media posts. In: Thirtieth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v30i1.10387"},{"issue":"2","key":"9873_CR44","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241\u2013259","journal-title":"Neural Netw"},{"key":"9873_CR45","unstructured":"Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: Icml, vol 97, pp 412\u2013420"},{"key":"9873_CR46","unstructured":"Zeiler MD (2012) Adadelta: an adaptive learning rate method. arXiv:1212.5701"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09873-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09873-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09873-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T05:59:31Z","timestamp":1744178371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09873-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,25]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["9873"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09873-8","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,25]]},"assertion":[{"value":"12 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}