{"id":"https://openalex.org/W4318148071","doi":"https://doi.org/10.1109/bigdata55660.2022.10020720","title":"Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets","display_name":"Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4318148071","doi":"https://doi.org/10.1109/bigdata55660.2022.10020720"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020720","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020720","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/A5054525289","display_name":"Yejun Soun","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yejun Soun","raw_affiliation_strings":["Seoul National University","DeepTrade"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"DeepTrade","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036321139","display_name":"Jaemin Yoo","orcid":"https://orcid.org/0000-0001-7237-5117"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jaemin Yoo","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047592616","display_name":"Min-Yong Cho","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109800","display_name":"Primary Source","ror":"https://ror.org/01x3s6v81","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210109800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Minyong Cho","raw_affiliation_strings":["Deeping Source"],"affiliations":[{"raw_affiliation_string":"Deeping Source","institution_ids":["https://openalex.org/I4210109800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062103675","display_name":"Jihyeong Jeon","orcid":"https://orcid.org/0009-0009-3002-0743"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jihyeong Jeon","raw_affiliation_strings":["Seoul National University","DeepTrade"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]},{"raw_affiliation_string":"DeepTrade","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074561505","display_name":"U Kang","orcid":"https://orcid.org/0000-0002-8774-6950"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"U Kang","raw_affiliation_strings":["Seoul National University"],"affiliations":[{"raw_affiliation_string":"Seoul National University","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5054525289"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":7.7851,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.98518519,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1691","last_page":"1700"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9908000230789185,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9825999736785889,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7515890598297119},{"id":"https://openalex.org/keywords/stock","display_name":"Stock (firearms)","score":0.6975350379943848},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5402161478996277},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5254249572753906},{"id":"https://openalex.org/keywords/stock-price","display_name":"Stock price","score":0.48641490936279297},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.480409175157547},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32643961906433105},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06715703010559082}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7515890598297119},{"id":"https://openalex.org/C204036174","wikidata":"https://www.wikidata.org/wiki/Q909380","display_name":"Stock (firearms)","level":2,"score":0.6975350379943848},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5402161478996277},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5254249572753906},{"id":"https://openalex.org/C2988984586","wikidata":"https://www.wikidata.org/wiki/Q1020013","display_name":"Stock price","level":3,"score":0.48641490936279297},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.480409175157547},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32643961906433105},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06715703010559082},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020720","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020720","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1980836123","https://openalex.org/W2034365297","https://openalex.org/W2066795664","https://openalex.org/W2109553965","https://openalex.org/W2126267628","https://openalex.org/W2131744502","https://openalex.org/W2157331557","https://openalex.org/W2171468534","https://openalex.org/W2250886571","https://openalex.org/W2296438605","https://openalex.org/W2510046892","https://openalex.org/W2575002693","https://openalex.org/W2613328025","https://openalex.org/W2734986640","https://openalex.org/W2744043447","https://openalex.org/W2774559076","https://openalex.org/W2798413829","https://openalex.org/W2883599634","https://openalex.org/W2896309423","https://openalex.org/W2896421350","https://openalex.org/W2896457183","https://openalex.org/W2897257904","https://openalex.org/W2897288200","https://openalex.org/W2952277250","https://openalex.org/W2954731415","https://openalex.org/W2963250244","https://openalex.org/W2964413206","https://openalex.org/W2965446444","https://openalex.org/W2998454530","https://openalex.org/W3034478396","https://openalex.org/W3035389441","https://openalex.org/W3035414307","https://openalex.org/W3046975451","https://openalex.org/W3094541172","https://openalex.org/W3131543199","https://openalex.org/W3154141829","https://openalex.org/W3170487013","https://openalex.org/W3185208886","https://openalex.org/W3190587204","https://openalex.org/W3198316914","https://openalex.org/W3210936843","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6636510571","https://openalex.org/W6679775712","https://openalex.org/W6697136110","https://openalex.org/W6732339902","https://openalex.org/W6739901393","https://openalex.org/W6755207826","https://openalex.org/W6764679822","https://openalex.org/W6779016441","https://openalex.org/W6798449921"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W4285260836","https://openalex.org/W3046775127","https://openalex.org/W3170094116","https://openalex.org/W4205958290","https://openalex.org/W3107474891"],"abstract_inverted_index":{"Given":[0],"historical":[1],"stock":[2,12,27,53,107,127,221],"prices":[3],"and":[4,30,75,96,150],"sparse":[5],"tweets,":[6,189],"how":[7],"can":[8],"we":[9,111],"accurately":[10],"predict":[11],"price":[13,28,83,94],"movement?":[14],"Many":[15],"market":[16],"analysts":[17],"strive":[18],"to":[19,135,164,169,202],"use":[20,49,165],"a":[21,71],"large":[22],"amount":[23],"of":[24,34,38,43,64,92,116,139,148,205,220],"information":[25,39,90,99],"for":[26,81,118,126,173,197],"prediction,":[29,198],"Twitter":[31],"is":[32,66,102],"one":[33],"the":[35,62,137,153,171,178,203,217],"richest":[36],"sources":[37],"presenting":[40],"real-time":[41],"opinions":[42],"people.":[44],"However,":[45],"previous":[46,140],"works":[47],"that":[48,214],"tweet":[50],"data":[51],"in":[52,152],"movement":[54,128,222],"prediction":[55,172],"have":[56,78],"suffered":[57],"from":[58,100,188],"two":[59,132],"limitations.":[60],"First,":[61,143],"number":[63],"tweets":[65,87,101,151,168],"heavily":[67],"biased":[68],"towards":[69],"only":[70],"few":[72],"popular":[73],"stocks,":[74,176],"most":[76],"stocks":[77,149,187],"insufficient":[79],"evidence":[80,196],"accurate":[82,124],"prediction.":[84,129,223],"Second,":[85,181],"many":[86],"provide":[88],"noisy":[89],"irrelevant":[91],"actual":[93],"movement,":[95],"extracting":[97],"reliable":[98],"as":[103,105,194],"challenging":[104],"predicting":[106],"prices.In":[108],"this":[109],"paper,":[110],"propose":[112],"SLOT":[113,130,144,182,215],"(Self-supervised":[114],"Learning":[115],"Tweets":[117],"Capturing":[119],"Multi-level":[120],"Price":[121],"Trends),":[122],"an":[123],"method":[125],"has":[131],"main":[133],"ideas":[134],"address":[136],"limitations":[138],"tweet-based":[141],"models.":[142],"learns":[145,183],"embedding":[146],"vectors":[147],"same":[154],"semantic":[155],"space":[156],"through":[157],"self-supervised":[158],"learning.":[159],"The":[160],"embeddings":[161],"allow":[162],"us":[163],"all":[166],"available":[167],"improve":[170],"even":[174],"unpopular":[175],"addressing":[177],"sparsity":[179],"problem.":[180],"multi-level":[184],"relationships":[185],"between":[186],"rather":[190],"than":[191],"using":[192],"them":[193],"direct":[195],"making":[199],"it":[200],"robust":[201],"unreliability":[204],"tweets.":[206],"Extensive":[207],"experiments":[208],"on":[209],"real":[210],"world":[211],"datasets":[212],"show":[213],"provides":[216],"state-of-the-art":[218],"accuracy":[219]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
