{"id":"https://openalex.org/W2585796967","doi":"https://doi.org/10.1109/bigdata.2016.7840875","title":"Predicting the shape and peak time of news article views","display_name":"Predicting the shape and peak time of news article views","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2585796967","doi":"https://doi.org/10.1109/bigdata.2016.7840875","mag":"2585796967"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2016.7840875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012282263","display_name":"Yaser Keneshloo","orcid":"https://orcid.org/0000-0002-1962-2542"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yaser Keneshloo","raw_affiliation_strings":["Discovery Analytics Center, Virginia Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Discovery Analytics Center, Virginia Tech","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100756049","display_name":"Shuguang Wang","orcid":"https://orcid.org/0000-0002-4586-7666"},"institutions":[{"id":"https://openalex.org/I139347817","display_name":"United States Postal Service","ror":"https://ror.org/008g9p546","country_code":"US","type":"government","lineage":["https://openalex.org/I139347817"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuguang Wang","raw_affiliation_strings":["The Washington Post, Washington D.C"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Washington Post, Washington D.C","institution_ids":["https://openalex.org/I139347817"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091491441","display_name":"Eui-Hong Han","orcid":null},"institutions":[{"id":"https://openalex.org/I139347817","display_name":"United States Postal Service","ror":"https://ror.org/008g9p546","country_code":"US","type":"government","lineage":["https://openalex.org/I139347817"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eui-Hong Sam Han","raw_affiliation_strings":["The Washington Post, Washington D.C"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Washington Post, Washington D.C","institution_ids":["https://openalex.org/I139347817"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035052603","display_name":"Naren Ramakrishnan","orcid":"https://orcid.org/0000-0002-1821-9743"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naren Ramakrishnan","raw_affiliation_strings":["Discovery Analytics Center, Virginia Tech"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Discovery Analytics Center, Virginia Tech","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2400","last_page":"2409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10799","display_name":"Data Visualization and Analytics","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/popularity","display_name":"Popularity","score":0.8395066261291504},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.7377820014953613},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7143726348876953},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.5611032247543335},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.558421790599823},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5325307846069336},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4607921540737152},{"id":"https://openalex.org/keywords/promotion","display_name":"Promotion (chess)","score":0.45059216022491455},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32469987869262695},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32225871086120605},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.2492293119430542},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10054442286491394},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.08542773127555847}],"concepts":[{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.8395066261291504},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.7377820014953613},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7143726348876953},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.5611032247543335},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.558421790599823},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5325307846069336},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4607921540737152},{"id":"https://openalex.org/C98147612","wikidata":"https://www.wikidata.org/wiki/Q215599","display_name":"Promotion (chess)","level":3,"score":0.45059216022491455},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32469987869262695},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32225871086120605},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2492293119430542},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10054442286491394},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.08542773127555847},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2016.7840875","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840875","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1500063156","https://openalex.org/W1521478692","https://openalex.org/W1538076060","https://openalex.org/W1752870744","https://openalex.org/W1972833205","https://openalex.org/W1987971958","https://openalex.org/W1996263819","https://openalex.org/W2017977879","https://openalex.org/W2037537012","https://openalex.org/W2045067045","https://openalex.org/W2047358712","https://openalex.org/W2052817812","https://openalex.org/W2073689275","https://openalex.org/W2079646068","https://openalex.org/W2097343308","https://openalex.org/W2107884625","https://openalex.org/W2112056172","https://openalex.org/W2141250202","https://openalex.org/W2166008115","https://openalex.org/W2190570825","https://openalex.org/W2507324087","https://openalex.org/W2962860893","https://openalex.org/W3104075975","https://openalex.org/W3104987177","https://openalex.org/W3125952634","https://openalex.org/W3143839440","https://openalex.org/W4238937169","https://openalex.org/W4299802090","https://openalex.org/W6632258966","https://openalex.org/W6674618218","https://openalex.org/W6687108098","https://openalex.org/W6725036847"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2477036161","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W4294565801","https://openalex.org/W2093123876","https://openalex.org/W4388192780"],"abstract_inverted_index":{"Predicting":[0],"the":[1,35,122],"popularity":[2],"of":[3,37,56,72,91,105,125],"news":[4,38,62],"articles":[5,63],"-":[6,14],"whether":[7],"measured":[8],"via":[9],"retweets,":[10],"clicks,":[11],"or":[12,95],"views":[13,73,133],"is":[15,142],"an":[16,48,84,143],"important":[17],"problem":[18],"for":[19,61],"editors,":[20],"journalists,":[21],"and":[22,41,79,98,120,134,152,161],"readers":[23],"alike.":[24],"In":[25],"this":[26,43],"paper,":[27],"we":[28],"introduce":[29],"a":[30,70],"new":[31],"model":[32,44],"to":[33,45,102,111,128,156],"predict":[34,129],"shape":[36,104],"article":[39,49,126,158],"views,":[40,106],"use":[42],"determine":[46],"when":[47,69],"will":[50,74],"likely":[51],"reach":[52],"its":[53,130],"maximum":[54],"number":[55],"views.":[57],"Although":[58],"volume":[59],"prediction":[60,127],"has":[64],"been":[65],"extensively":[66],"studied":[67],"predicting":[68],"burst":[71],"happen,":[75],"in":[76],"what":[77],"shape,":[78],"by":[80],"how":[81],"much,":[82],"remains":[83],"open":[85],"problem.":[86],"We":[87],"engineer":[88],"several":[89],"classes":[90],"features":[92],"(metadata,":[93],"contextual":[94],"content-based,":[96],"temporal,":[97],"social),":[99],"develop":[100],"models":[101],"classify":[103],"with":[107],"particular":[108],"attention":[109],"paid":[110],"performing":[112],"online,":[113],"time-updated,":[114],"prediction,":[115],"i.e.,":[116],"using":[117],"data":[118],"before":[119],"during":[121],"early":[123],"stages":[124],"eventual":[131],"peak":[132],"update":[135],"earlier":[136],"predictions.":[137],"The":[138,149],"system":[139],"presented":[140],"here":[141],"emerging":[144],"application":[145],"being":[146],"developed":[147],"at":[148],"Washington":[150],"Post":[151],"can":[153],"be":[154],"used":[155],"support":[157],"placement,":[159],"updating,":[160],"promotion":[162],"strategies.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}