{"id":"https://openalex.org/W2782204918","doi":"https://doi.org/10.1109/bigdata.2017.8258464","title":"Identifying emergency stages in facebook posts of police departments with convolutional and recurrent neural networks and support vector machines","display_name":"Identifying emergency stages in facebook posts of police departments with convolutional and recurrent neural networks and support vector machines","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2782204918","doi":"https://doi.org/10.1109/bigdata.2017.8258464","mag":"2782204918"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2017.8258464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.cbs.dk/en/publications/b02d47fd-b20f-45c1-a8cf-a394be75bf78","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025257282","display_name":"Nicolai Pogrebnyakov","orcid":"https://orcid.org/0000-0003-3574-1194"},"institutions":[{"id":"https://openalex.org/I180519160","display_name":"Copenhagen Business School","ror":"https://ror.org/04sppb023","country_code":"DK","type":"education","lineage":["https://openalex.org/I180519160"]}],"countries":["DK"],"is_corresponding":true,"raw_author_name":"Nicolai Pogrebnyakov","raw_affiliation_strings":["Copenhagen Business School, Frederiksberg, Denmark"],"affiliations":[{"raw_affiliation_string":"Copenhagen Business School, Frederiksberg, Denmark","institution_ids":["https://openalex.org/I180519160"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110707122","display_name":"Edgar Maldonado","orcid":null},"institutions":[{"id":"https://openalex.org/I879689382","display_name":"Metropolitan State University of Denver","ror":"https://ror.org/03mnxwj46","country_code":"US","type":"education","lineage":["https://openalex.org/I879689382"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edgar Maldonado","raw_affiliation_strings":["Metropolitan State University of Denver, Denver, CO, United States"],"affiliations":[{"raw_affiliation_string":"Metropolitan State University of Denver, Denver, CO, United States","institution_ids":["https://openalex.org/I879689382"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5025257282"],"corresponding_institution_ids":["https://openalex.org/I180519160"],"apc_list":null,"apc_paid":null,"fwci":1.0385,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.83939206,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4343","last_page":"4352"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9921000003814697,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.9735310077667236},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7502390742301941},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7312508821487427},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.689311146736145},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6550149917602539},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.6294130086898804},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5573597550392151},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5504072308540344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4855194091796875},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35629791021347046},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.29224130511283875}],"concepts":[{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.9735310077667236},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7502390742301941},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7312508821487427},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.689311146736145},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6550149917602539},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.6294130086898804},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5573597550392151},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5504072308540344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4855194091796875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35629791021347046},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.29224130511283875},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/bigdata.2017.8258464","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2017.8258464","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:research-api.cbs.dk:openaire_cris_publications/b02d47fd-b20f-45c1-a8cf-a394be75bf78","is_oa":true,"landing_page_url":"https://research.cbs.dk/en/publications/b02d47fd-b20f-45c1-a8cf-a394be75bf78","pdf_url":null,"source":{"id":"https://openalex.org/S4306401458","display_name":"CBS Research Portal (Copenhagen Business School)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180519160","host_organization_name":"Copenhagen Business School","host_organization_lineage":["https://openalex.org/I180519160"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Pogrebnyakov, N & Maldonado, E 2017, Identifying Emergency Stages in Facebook Posts of Police Departments with Convolutional and Recurrent Neural Networks and Support Vector Machines. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017. IEEE, Los Alamitos, CA, pp. 4343-4352, Fifth IEEE International Conference on Big Data. IEEE BigData 2017, Boston, Massachusetts, United States, 11/12/2017. https://doi.org/10.1109/BigData.2017.8258464","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:arXiv.org:1801.00801","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1801.00801","pdf_url":"https://arxiv.org/pdf/1801.00801","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pure.atira.dk:Publications/b02d47fd-b20f-45c1-a8cf-a394be75bf78","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401458","display_name":"CBS Research Portal (Copenhagen Business School)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180519160","host_organization_name":"Copenhagen Business School","host_organization_lineage":["https://openalex.org/I180519160"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":""}],"best_oa_location":{"id":"pmh:oai:research-api.cbs.dk:openaire_cris_publications/b02d47fd-b20f-45c1-a8cf-a394be75bf78","is_oa":true,"landing_page_url":"https://research.cbs.dk/en/publications/b02d47fd-b20f-45c1-a8cf-a394be75bf78","pdf_url":null,"source":{"id":"https://openalex.org/S4306401458","display_name":"CBS Research Portal (Copenhagen Business School)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I180519160","host_organization_name":"Copenhagen Business School","host_organization_lineage":["https://openalex.org/I180519160"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Pogrebnyakov, N & Maldonado, E 2017, Identifying Emergency Stages in Facebook Posts of Police Departments with Convolutional and Recurrent Neural Networks and Support Vector Machines. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings. 2017 IEEE International Conference on Big Data : IEEE Big Data 2017. IEEE, Los Alamitos, CA, pp. 4343-4352, Fifth IEEE International Conference on Big Data. IEEE BigData 2017, Boston, Massachusetts, United States, 11/12/2017. https://doi.org/10.1109/BigData.2017.8258464","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W50740790","https://openalex.org/W235909202","https://openalex.org/W255593144","https://openalex.org/W1487925325","https://openalex.org/W1518146043","https://openalex.org/W1569590524","https://openalex.org/W1614298861","https://openalex.org/W1832693441","https://openalex.org/W1849277567","https://openalex.org/W1880262756","https://openalex.org/W1934362406","https://openalex.org/W1966443646","https://openalex.org/W1971494700","https://openalex.org/W2007396488","https://openalex.org/W2015503141","https://openalex.org/W2015861736","https://openalex.org/W2041653115","https://openalex.org/W2064675550","https://openalex.org/W2073553076","https://openalex.org/W2074982482","https://openalex.org/W2095705004","https://openalex.org/W2097998348","https://openalex.org/W2098825757","https://openalex.org/W2107878631","https://openalex.org/W2113459411","https://openalex.org/W2116619054","https://openalex.org/W2118020653","https://openalex.org/W2131744502","https://openalex.org/W2141599568","https://openalex.org/W2143612262","https://openalex.org/W2144499799","https://openalex.org/W2149684865","https://openalex.org/W2154359981","https://openalex.org/W2157331557","https://openalex.org/W2163605009","https://openalex.org/W2234752194","https://openalex.org/W2339534575","https://openalex.org/W2552282916","https://openalex.org/W2564900545","https://openalex.org/W2574087343","https://openalex.org/W2585816810","https://openalex.org/W2593887162","https://openalex.org/W2595741287","https://openalex.org/W2801151396","https://openalex.org/W2917065785","https://openalex.org/W2919115771","https://openalex.org/W2950577311","https://openalex.org/W2951255933","https://openalex.org/W2952965972","https://openalex.org/W2962707464","https://openalex.org/W2964331270","https://openalex.org/W4231510805","https://openalex.org/W4234557866","https://openalex.org/W4285719527","https://openalex.org/W4294238563","https://openalex.org/W6609665086","https://openalex.org/W6633862164","https://openalex.org/W6639204139","https://openalex.org/W6639619044","https://openalex.org/W6674330103","https://openalex.org/W6674385629","https://openalex.org/W6676984168","https://openalex.org/W6679775712","https://openalex.org/W6680890276","https://openalex.org/W6682839988","https://openalex.org/W6684191040","https://openalex.org/W6732320445"],"related_works":["https://openalex.org/W2980729574","https://openalex.org/W1560851690","https://openalex.org/W3092047717","https://openalex.org/W3110772647","https://openalex.org/W4390881630","https://openalex.org/W2770162183","https://openalex.org/W2947721150","https://openalex.org/W2894231409","https://openalex.org/W3127365535","https://openalex.org/W2995297654"],"abstract_inverted_index":{"Classification":[0],"of":[1,19,43,61,118],"social":[2],"media":[3],"posts":[4],"in":[5,32],"emergency":[6,63],"response":[7,65],"is":[8],"an":[9,103],"important":[10],"practical":[11],"problem:":[12],"accurate":[13],"classification":[14],"can":[15],"help":[16,23],"automate":[17],"processing":[18],"such":[20],"messages":[21,42,50],"and":[22,26,66,79,81,90,93],"other":[24],"responders":[25],"the":[27,115],"public":[28],"react":[29],"to":[30,53,110],"emergencies":[31],"a":[33,106],"timely":[34],"fashion.":[35],"This":[36],"research":[37],"focused":[38],"on":[39],"classifying":[40],"Facebook":[41],"US":[44],"police":[45],"departments.":[46],"Randomly":[47],"selected":[48],"5,000":[49],"were":[51,75,83],"used":[52],"train":[54],"classifiers":[55],"that":[56],"distinguished":[57],"between":[58],"four":[59],"categories":[60],"messages:":[62],"preparedness,":[64],"recovery,":[67],"as":[68,70],"well":[69],"general":[71],"engagement":[72],"messages.":[73],"Features":[74],"represented":[76],"with":[77,105],"bag-of-words":[78],"word2vec,":[80],"models":[82],"constructed":[84],"using":[85],"support":[86],"vector":[87],"machines":[88],"(SVMs)":[89],"convolutional":[91],"(CNNs)":[92],"recurrent":[94],"neural":[95],"networks":[96],"(RNNs).":[97],"The":[98],"best":[99],"performing":[100],"classifier":[101],"was":[102],"RNN":[104],"custom-trained":[107],"word2vec":[108],"model":[109],"represent":[111],"features,":[112],"which":[113],"achieved":[114],"F1":[116],"measure":[117],"0.839.":[119]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}