{"id":"https://openalex.org/W3193354450","doi":"https://doi.org/10.1145/3485447.3512285","title":"Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling","display_name":"Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W3193354450","doi":"https://doi.org/10.1145/3485447.3512285","mag":"3193354450"},"language":"en","primary_location":{"id":"doi:10.1145/3485447.3512285","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3512285","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3512285","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","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 ACM Web Conference 2022","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3512285","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100702888","display_name":"Lingzhi Wang","orcid":"https://orcid.org/0000-0002-1346-2437"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Lingzhi Wang","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336998","display_name":"Jing Li","orcid":"https://orcid.org/0000-0002-8044-2284"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":["The Hong Kong Polytechnic University, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044933782","display_name":"Xingshan Zeng","orcid":"https://orcid.org/0000-0002-0455-5519"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingshan Zeng","raw_affiliation_strings":["Huawei Noah's Ark Lab, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah's Ark Lab, Hong Kong","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008208316","display_name":"Kam\u2010Fai Wong","orcid":"https://orcid.org/0000-0002-9427-5659"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Kam-Fai Wong","raw_affiliation_strings":["The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4153,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.54826398,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1663","last_page":"1672"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9987000226974487,"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":0.9987000226974487,"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":0.9980999827384949,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9968000054359436,"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/computer-science","display_name":"Computer science","score":0.6856157183647156},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.5783689022064209},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41545066237449646},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.37069112062454224},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32180333137512207}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6856157183647156},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.5783689022064209},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41545066237449646},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.37069112062454224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32180333137512207}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3485447.3512285","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3512285","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3512285","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","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 ACM Web Conference 2022","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3485447.3512285","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3485447.3512285","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3485447.3512285","source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","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 ACM Web Conference 2022","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335581","display_name":"Young Scientists Fund","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3193354450.pdf","grobid_xml":"https://content.openalex.org/works/W3193354450.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W23224015","https://openalex.org/W1654173042","https://openalex.org/W1665214252","https://openalex.org/W1853851539","https://openalex.org/W1859339560","https://openalex.org/W1880262756","https://openalex.org/W2008634887","https://openalex.org/W2026318959","https://openalex.org/W2038043464","https://openalex.org/W2071106879","https://openalex.org/W2078283455","https://openalex.org/W2095705004","https://openalex.org/W2096008199","https://openalex.org/W2111214786","https://openalex.org/W2119821739","https://openalex.org/W2127267264","https://openalex.org/W2132827946","https://openalex.org/W2137986939","https://openalex.org/W2143017621","https://openalex.org/W2150523688","https://openalex.org/W2151954059","https://openalex.org/W2153222072","https://openalex.org/W2156876426","https://openalex.org/W2166771689","https://openalex.org/W2168709143","https://openalex.org/W2225156818","https://openalex.org/W2250539671","https://openalex.org/W2264102947","https://openalex.org/W2340452757","https://openalex.org/W2505102643","https://openalex.org/W2593390416","https://openalex.org/W2605659599","https://openalex.org/W2756882086","https://openalex.org/W2803119452","https://openalex.org/W2883617657","https://openalex.org/W2921642681","https://openalex.org/W2949963192","https://openalex.org/W2950635152","https://openalex.org/W2951004968","https://openalex.org/W2951128894","https://openalex.org/W2952100657","https://openalex.org/W2952478253","https://openalex.org/W2953320089","https://openalex.org/W2962755817","https://openalex.org/W2963341956","https://openalex.org/W2963367922","https://openalex.org/W2964015378","https://openalex.org/W2964121744","https://openalex.org/W2970671833","https://openalex.org/W2970683844","https://openalex.org/W2970796111","https://openalex.org/W2972590764","https://openalex.org/W3012564649","https://openalex.org/W3101380508","https://openalex.org/W3105602332","https://openalex.org/W3173589978","https://openalex.org/W3196845929","https://openalex.org/W4200108907"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"With":[0],"the":[1,29,62,70,114,125,143,188],"increasing":[2],"popularity":[3],"of":[4,113],"social":[5,120],"media,":[6],"online":[7,78,117],"interpersonal":[8],"communication":[9],"now":[10],"plays":[11],"an":[12,110,134],"essential":[13],"role":[14],"in":[15,28,39,77,98,116,209],"people\u2019s":[16],"everyday":[17],"information":[18],"exchange.":[19],"Whether":[20],"and":[21,74,119,146,153,163,178,190,196],"how":[22,205],"a":[23,82,88,99],"newcomer":[24],"can":[25],"better":[26,207],"engage":[27,75],"community":[30],"has":[31],"attracted":[32],"great":[33],"interest":[34],"due":[35],"to":[36,85,94,141,167,206],"its":[37],"application":[38],"many":[40],"scenarios.":[41],"Although":[42],"some":[43],"prior":[44],"works":[45],"that":[46,182],"explore":[47],"early":[48,71],"socialization":[49,72],"have":[50],"obtained":[51],"salient":[52],"achievements,":[53],"they":[54],"are":[55,165],"focusing":[56],"on":[57,61,171,175,199,204],"sociological":[58],"surveys":[59],"based":[60],"small":[63],"group.":[64],"To":[65,122],"help":[66],"individuals":[67],"get":[68],"through":[69],"period":[73],"well":[76],"conversations,":[79],"we":[80,132],"study":[81],"novel":[83],"task":[84,107],"foresee":[86],"whether":[87],"newcomer\u2019s":[89,150],"message":[90],"will":[91],"be":[92,109],"responded":[93],"by":[95],"other":[96],"participants":[97],"multi-party":[100],"conversation":[101],"(henceforth":[102],"Successful":[103],"New-entry":[104],"Prediction)1.":[105],"The":[106],"would":[108],"important":[111],"part":[112],"research":[115,170],"assistants":[118],"media.":[121],"further":[123,169],"investigate":[124],"key":[126],"factors":[127],"indicating":[128],"such":[129],"engagement":[130],"success,":[131],"employ":[133],"unsupervised":[135],"neural":[136,192],"network,":[137],"Variational":[138],"Auto-Encoder":[139],"(VAE),":[140],"examine":[142],"topic":[144],"content":[145],"discourse":[147],"behavior":[148,201],"from":[149,161],"chatting":[151],"history":[152],"conversation\u2019s":[154],"ongoing":[155],"context.":[156],"Furthermore,":[157],"two":[158],"large-scale":[159],"datasets,":[160],"Reddit":[162,179],"Twitter,":[164],"collected":[166],"support":[168],"new-entries.":[172],"Extensive":[173],"experiments":[174],"both":[176],"Twitter":[177],"datasets":[180],"show":[181],"our":[183],"model":[184],"significantly":[185],"outperforms":[186],"all":[187],"baselines":[189],"popular":[191],"models.":[193],"Additional":[194],"explainable":[195],"visual":[197],"analyses":[198],"new-entry":[200],"shed":[202],"light":[203],"join":[208],"others\u2019":[210],"discussions.":[211]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
