{"id":"https://openalex.org/W4304014444","doi":"https://doi.org/10.1145/3503161.3551607","title":"An Efficient Multi-View Multimodal Data Processing Framework for Social Media Popularity Prediction","display_name":"An Efficient Multi-View Multimodal Data Processing Framework for Social Media Popularity Prediction","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4304014444","doi":"https://doi.org/10.1145/3503161.3551607"},"language":"en","primary_location":{"id":"doi:10.1145/3503161.3551607","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3551607","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"Proceedings of the 30th ACM International Conference on Multimedia","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/A5017360201","display_name":"Yunpeng Tan","orcid":"https://orcid.org/0000-0002-3326-1835"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"YunPeng Tan","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101576479","display_name":"Fangyu Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangyu Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005278138","display_name":"BoWei Li","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"BoWei Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100459168","display_name":"Zheng Zhang","orcid":"https://orcid.org/0000-0003-1470-6998"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100335260","display_name":"Bo Zhang","orcid":"https://orcid.org/0000-0002-2289-2877"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":2.3599,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.90681981,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"7200","last_page":"7204"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9991999864578247,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9991999864578247,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9990000128746033,"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/T10028","display_name":"Topic Modeling","score":0.9965999722480774,"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/popularity","display_name":"Popularity","score":0.9683061242103577},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7825534343719482},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.7741946578025818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49646979570388794},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.49330583214759827},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48883479833602905},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.48877832293510437},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4176216721534729},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3582296371459961},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.26015666127204895},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.1441631019115448}],"concepts":[{"id":"https://openalex.org/C2780586970","wikidata":"https://www.wikidata.org/wiki/Q1357284","display_name":"Popularity","level":2,"score":0.9683061242103577},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825534343719482},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.7741946578025818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49646979570388794},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.49330583214759827},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48883479833602905},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.48877832293510437},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4176216721534729},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3582296371459961},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.26015666127204895},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.1441631019115448},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3503161.3551607","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3503161.3551607","pdf_url":null,"source":{"id":"https://openalex.org/S4363608757","display_name":"Proceedings of the 30th ACM International Conference on Multimedia","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":"Proceedings of the 30th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8139811084","display_name":"\u201c\u4eba-\u673a-\u7269\u201d\u534f\u540c\u611f\u77e5\u4e2d\u5f02\u6784\u611f\u77e5\u8282\u70b9\u9009\u62e9\u4e0e\u8c03\u5ea6\u673a\u5236\u7814\u7a76","funder_award_id":"61802022","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1880262756","https://openalex.org/W1978394996","https://openalex.org/W2090059852","https://openalex.org/W2250539671","https://openalex.org/W2295598076","https://openalex.org/W2472257696","https://openalex.org/W2493916176","https://openalex.org/W2556468274","https://openalex.org/W2766233751","https://openalex.org/W2808041471","https://openalex.org/W2963603520","https://openalex.org/W2981384194","https://openalex.org/W2981412311","https://openalex.org/W3035443989","https://openalex.org/W3093330054","https://openalex.org/W3093352728","https://openalex.org/W3185713948","https://openalex.org/W4206125592","https://openalex.org/W4312877428","https://openalex.org/W4313160444"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2368605798","https://openalex.org/W2518037665","https://openalex.org/W2348524959","https://openalex.org/W2368049389","https://openalex.org/W2384861574","https://openalex.org/W2093123876","https://openalex.org/W2994850404","https://openalex.org/W3043432080","https://openalex.org/W4317600379"],"abstract_inverted_index":{"Popularity":[0],"of":[1,8,13,63,71,85],"social":[2,14,21,43,86,111],"media":[3,15,44,87,112],"is":[4],"an":[5,28],"important":[6,76],"symbol":[7],"its":[9],"communication":[10],"power.":[11],"Predictions":[12],"popularity":[16,49,113],"have":[17],"tremendous":[18],"business":[19],"and":[20,46,54,74],"value.":[22],"In":[23],"this":[24],"paper,":[25],"we":[26],"propose":[27],"efficient":[29],"multimodal":[30,42],"data":[31,45],"processing":[32],"framework,":[33],"which":[34],"can":[35],"comprehensively":[36],"extract":[37,59],"the":[38,69,95,110],"multi-view":[39],"features":[40,62,77],"from":[41,79],"achieve":[47],"accurate":[48,83],"prediction.":[50],"We":[51,89],"utilize":[52,65],"Transformer":[53],"sliding":[55],"window":[56],"average":[57],"to":[58,67],"time":[60],"series":[61],"posts,":[64],"CatBoost":[66],"calculate":[68],"importance":[70],"different":[72],"features,":[73],"integrate":[75],"extracted":[78],"multiple":[80],"views":[81],"for":[82],"prediction":[84,114],"popularity.":[88],"evaluate":[90],"our":[91,104],"proposed":[92],"approach":[93,105],"with":[94],"Social":[96],"Media":[97],"Prediction":[98],"Dataset.":[99],"Experimental":[100],"results":[101],"show":[102],"that":[103],"achieves":[106],"excellent":[107],"performance":[108],"in":[109],"task.":[115]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":5}],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2025-10-10T00:00:00"}
