{"id":"https://openalex.org/W4318147622","doi":"https://doi.org/10.1109/bigdata55660.2022.10020618","title":"Analysis and Estimation of News Article Reading Time with Multimodal Machine Learning","display_name":"Analysis and Estimation of News Article Reading Time with Multimodal Machine Learning","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318147622","doi":"https://doi.org/10.1109/bigdata55660.2022.10020618"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020618","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","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/A5034011295","display_name":"Shotaro Ishihara","orcid":"https://orcid.org/0009-0001-0366-6807"},"institutions":[{"id":"https://openalex.org/I4210114208","display_name":"Nikkei Business Publications (Japan)","ror":"https://ror.org/0220t3t55","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210114208"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shotaro Ishihara","raw_affiliation_strings":["Nikkei, Inc,Tokyo,Japan","Nikkei, Inc, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nikkei, Inc,Tokyo,Japan","institution_ids":["https://openalex.org/I4210114208"]},{"raw_affiliation_string":"Nikkei, Inc, Tokyo, Japan","institution_ids":["https://openalex.org/I4210114208"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067553376","display_name":"Yasufumi Nakama","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yasufumi Nakama","raw_affiliation_strings":["Independent Researcher,Tokyo,Japan","Independent Researcher, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Researcher,Tokyo,Japan","institution_ids":[]},{"raw_affiliation_string":"Independent Researcher, Tokyo, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.059,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.3508199,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"2265","last_page":"2268"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9976000189781189,"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"}},"topics":[{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9976000189781189,"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"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9966999888420105,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9940000176429749,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8180229663848877},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.74237060546875},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6459410786628723},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.554932177066803},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5290272235870361},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5174461007118225},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4816931188106537},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4632934331893921},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35809481143951416},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32440805435180664},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.09260749816894531}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8180229663848877},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.74237060546875},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6459410786628723},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.554932177066803},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5290272235870361},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5174461007118225},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4816931188106537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4632934331893921},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35809481143951416},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32440805435180664},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.09260749816894531},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020618","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8799999952316284}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2014725748","https://openalex.org/W2058124204","https://openalex.org/W2064675550","https://openalex.org/W2166237624","https://openalex.org/W2407291067","https://openalex.org/W2518108298","https://openalex.org/W2531563875","https://openalex.org/W2743333519","https://openalex.org/W2950973634","https://openalex.org/W2963263347","https://openalex.org/W2963587345","https://openalex.org/W3034518013","https://openalex.org/W3035333188","https://openalex.org/W3138516171","https://openalex.org/W3152876231","https://openalex.org/W3175306105","https://openalex.org/W3175854873","https://openalex.org/W4205421901","https://openalex.org/W4287887501","https://openalex.org/W6732366198","https://openalex.org/W6801655670"],"related_works":["https://openalex.org/W2082438799","https://openalex.org/W1966986837","https://openalex.org/W2360138227","https://openalex.org/W4365808155","https://openalex.org/W1838455177","https://openalex.org/W2392697679","https://openalex.org/W2385990477","https://openalex.org/W2331964906","https://openalex.org/W2067734110","https://openalex.org/W2605711304"],"abstract_inverted_index":{"This":[0],"paper":[1],"highlights":[2],"the":[3,13,39,104,107],"importance":[4],"of":[5,18,53],"reading":[6,20,43,60],"time":[7,21,44,61],"for":[8,32,38],"news":[9,56],"media":[10],"and":[11,26,57,95,97],"evaluates":[12],"implementation":[14,40],"methodology.":[15],"The":[16,35],"display":[17],"estimated":[19],"allows":[22],"users":[23,52],"to":[24,79,91,100],"select":[25],"view":[27],"articles":[28,105],"that":[29,42,59,72,88],"are":[30],"appropriate":[31],"their":[33],"situation.":[34],"simplest":[36],"hypothesis":[37],"is":[41],"correlates":[45],"with":[46,66],"text":[47,67],"length.":[48,68],"We":[49],"analyzed":[50],"real-world":[51],"Japanese":[54],"financial":[55],"revealed":[58],"does":[62],"not":[63],"strongly":[64],"correlate":[65],"Experiments":[69],"also":[70],"showed":[71],"a":[73,80],"multimodal":[74],"machine":[75],"learning":[76],"approach":[77],"leads":[78],"more":[81],"accurate":[82],"estimation.":[83],"Specifically,":[84],"fine-tuning":[85],"neural":[86],"networks":[87],"incorporated":[89],"LSTM":[90],"process":[92],"user":[93],"history":[94],"BERT":[96],"Swin":[98],"Transformer":[99],"acquire":[101],"embeddings":[102],"from":[103],"achieved":[106],"best":[108],"results.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}