{"id":"https://openalex.org/W4401612381","doi":"https://doi.org/10.1109/coins61597.2024.10622132","title":"Exploiting Multimodal Features and Deep Learning for Predicting Crowdfunding Successes","display_name":"Exploiting Multimodal Features and Deep Learning for Predicting Crowdfunding Successes","publication_year":2024,"publication_date":"2024-07-29","ids":{"openalex":"https://openalex.org/W4401612381","doi":"https://doi.org/10.1109/coins61597.2024.10622132"},"language":"en","primary_location":{"id":"doi:10.1109/coins61597.2024.10622132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coins61597.2024.10622132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","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/A5100337466","display_name":"Zijian Zhang","orcid":"https://orcid.org/0000-0003-1194-8334"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Zijian Zhang","raw_affiliation_strings":["City University of Hong Kong,Department of Information Systems,Kowloon Tong,Hong Kong SAR"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong,Department of Information Systems,Kowloon Tong,Hong Kong SAR","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084348345","display_name":"Raymond Y.K. Lau","orcid":"https://orcid.org/0000-0002-5751-4550"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Raymond Y.K. Lau","raw_affiliation_strings":["City University of Hong Kong,Department of Information Systems,Kowloon Tong,Hong Kong SAR"],"affiliations":[{"raw_affiliation_string":"City University of Hong Kong,Department of Information Systems,Kowloon Tong,Hong Kong SAR","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100337466"],"corresponding_institution_ids":["https://openalex.org/I168719708"],"apc_list":null,"apc_paid":null,"fwci":0.5559,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.73317608,"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11995","display_name":"FinTech, Crowdfunding, Digital Finance","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11995","display_name":"FinTech, Crowdfunding, Digital Finance","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10987","display_name":"Microfinance and Financial Inclusion","score":0.9796000123023987,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10068","display_name":"Technology Adoption and User Behaviour","score":0.9606999754905701,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6339854001998901},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.598731517791748},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5537049174308777},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41579461097717285},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3582429885864258}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6339854001998901},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.598731517791748},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5537049174308777},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41579461097717285},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3582429885864258}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/coins61597.2024.10622132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coins61597.2024.10622132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","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":23,"referenced_works":["https://openalex.org/W114572522","https://openalex.org/W1180947467","https://openalex.org/W1966585319","https://openalex.org/W1983903351","https://openalex.org/W2028378463","https://openalex.org/W2060164253","https://openalex.org/W2120747893","https://openalex.org/W2131774270","https://openalex.org/W2154234817","https://openalex.org/W2169064565","https://openalex.org/W2206373005","https://openalex.org/W2250539671","https://openalex.org/W2516821356","https://openalex.org/W2542632482","https://openalex.org/W2622046756","https://openalex.org/W2896457183","https://openalex.org/W2956407285","https://openalex.org/W3086462496","https://openalex.org/W3121716941","https://openalex.org/W3121993247","https://openalex.org/W3122061554","https://openalex.org/W3122951260","https://openalex.org/W3194726941"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Though":[0],"structured":[1],"and":[2,30,54,97,127,145,221],"textual":[3,47,169],"unstructured":[4],"data":[5],"were":[6],"examined":[7,23,42],"in":[8,73],"predicting":[9,36],"crowdfunding":[10,37,78,150,229],"successes,":[11],"multimodal":[12,28,136,219],"features":[13,29,44,72,137,191,220],"have":[14,22],"seldom":[15],"been":[16],"exploited.":[17],"In":[18,39],"this":[19],"research,":[20],"we":[21,41,60,112,157],"the":[24,68,75,93,98,105,165,173,200,204,213,216,226],"predictive":[25],"power":[26],"of":[27,70,77,95,100,168,184,207,218,228],"various":[31],"deep":[32,114,222],"learning":[33,115,223],"models":[34,116],"for":[35],"successes.":[38,230],"particular,":[40],"implicit":[43],"such":[45,117,138],"as":[46,118,139],"project":[48,50,55,86,140],"descriptions,":[49],"related":[51,56],"audio":[52,143],"clips,":[53,144],"video":[57,91,146],"clips.":[58],"First,":[59],"utilized":[61],"an":[62],"explanatory":[63],"statistical":[64],"method":[65],"to":[66,148,172,215,224],"identify":[67],"significance":[69],"explicit":[71],"explaining":[74],"variance":[76],"amounts.":[79],"Our":[80,161,209],"empirical":[81],"results":[82],"reveal":[83],"that":[84,156,164],"a":[85,90,154,181,196],"description":[87,141],"supplemented":[88],"with":[89,135],"clip,":[92],"number":[94,99],"backers,":[96],"projects":[101],"previously":[102],"supported":[103],"are":[104],"top":[106],"three":[107,193],"most":[108],"significant":[109],"features.":[110],"Second,":[111],"applied":[113],"convolutional":[119],"neural":[120,124],"networks":[121,125],"(CNNs),":[122],"recurrent":[123],"(RNNs),":[126],"Bidirectional":[128],"Encoder":[129],"Representations":[130],"from":[131,159,192],"Transformers":[132],"(BERT)":[133],"fed":[134,171],"text,":[142],"clips":[147],"predict":[149],"successes":[151],"based":[152],"on":[153],"dataset":[155],"crawled":[158],"Kickstarter.":[160],"experiments":[162],"show":[163],"prediction":[166,227],"accuracy":[167,179,206],"feature":[170,185],"TextCNN":[174,201],"model":[175,202],"achieves":[176,203],"relatively":[177],"high":[178],"when":[180],"single":[182],"modality":[183],"is":[186],"used.":[187],"By":[188],"combing":[189],"all":[190],"modalities":[194],"via":[195],"late":[197],"fusion":[198],"method,":[199],"best":[205],"82.2%.":[208],"research":[210],"work":[211],"opens":[212],"door":[214],"exploitation":[217],"improve":[225]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-23T23:11:35.936235","created_date":"2025-10-10T00:00:00"}