{"id":"https://openalex.org/W4400050542","doi":"https://doi.org/10.1140/epjds/s13688-024-00465-2","title":"Analyzing user reactions using relevance between location information of tweets and news articles","display_name":"Analyzing user reactions using relevance between location information of tweets and news articles","publication_year":2024,"publication_date":"2024-06-26","ids":{"openalex":"https://openalex.org/W4400050542","doi":"https://doi.org/10.1140/epjds/s13688-024-00465-2"},"language":"en","primary_location":{"id":"doi:10.1140/epjds/s13688-024-00465-2","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-024-00465-2","pdf_url":null,"source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPJ Data Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1140/epjds/s13688-024-00465-2","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100597563","display_name":"Yun-Tae Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yun-Tae Jin","raw_affiliation_strings":["Department of Industrial Engineering, Seoul National University of Science and Technology, 01811, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Seoul National University of Science and Technology, 01811, Seoul, South Korea","institution_ids":["https://openalex.org/I118373667"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050694256","display_name":"Jaebeom You","orcid":"https://orcid.org/0009-0003-9539-1875"},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"JaeBeom You","raw_affiliation_strings":["Graduate School of Data Science, Seoul National University of Science and Technology, 01811, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Data Science, Seoul National University of Science and Technology, 01811, Seoul, South Korea","institution_ids":["https://openalex.org/I118373667"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046718849","display_name":"Shoko Wakamiya","orcid":"https://orcid.org/0000-0002-9371-1340"},"institutions":[{"id":"https://openalex.org/I75917431","display_name":"Nara Institute of Science and Technology","ror":"https://ror.org/05bhada84","country_code":"JP","type":"education","lineage":["https://openalex.org/I75917431"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shoko Wakamiya","raw_affiliation_strings":["Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 6300192, Nara, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 6300192, Nara, Japan","institution_ids":["https://openalex.org/I75917431"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057012645","display_name":"Hyuk-Yoon Kwon","orcid":"https://orcid.org/0000-0002-1125-6533"},"institutions":[{"id":"https://openalex.org/I118373667","display_name":"Seoul National University of Science and Technology","ror":"https://ror.org/00chfja07","country_code":"KR","type":"education","lineage":["https://openalex.org/I118373667"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyuk-Yoon Kwon","raw_affiliation_strings":["Department of Industrial Engineering / Graduate School of Data Science, Seoul National University of Science and Technology, 01811, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-1125-6533","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering / Graduate School of Data Science, Seoul National University of Science and Technology, 01811, Seoul, South Korea","institution_ids":["https://openalex.org/I118373667"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5057012645"],"corresponding_institution_ids":["https://openalex.org/I118373667"],"apc_list":{"value":1790,"currency":"USD","value_usd":1790},"apc_paid":{"value":1790,"currency":"USD","value_usd":1790},"fwci":0.1913,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44258271,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"13","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.995199978351593,"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":0.9950000047683716,"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/relevance","display_name":"Relevance (law)","score":0.8223024606704712},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6780992746353149},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.662639319896698},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.519733190536499},{"id":"https://openalex.org/keywords/news-media","display_name":"News media","score":0.46037986874580383},{"id":"https://openalex.org/keywords/affect","display_name":"Affect (linguistics)","score":0.42500990629196167},{"id":"https://openalex.org/keywords/advertising","display_name":"Advertising","score":0.2892470359802246},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.19922536611557007},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.16358187794685364},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1263105869293213},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11124119162559509}],"concepts":[{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.8223024606704712},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6780992746353149},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.662639319896698},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.519733190536499},{"id":"https://openalex.org/C529147693","wikidata":"https://www.wikidata.org/wiki/Q1193236","display_name":"News media","level":2,"score":0.46037986874580383},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.42500990629196167},{"id":"https://openalex.org/C112698675","wikidata":"https://www.wikidata.org/wiki/Q37038","display_name":"Advertising","level":1,"score":0.2892470359802246},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.19922536611557007},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.16358187794685364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1263105869293213},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11124119162559509},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C46312422","wikidata":"https://www.wikidata.org/wiki/Q11024","display_name":"Communication","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1140/epjds/s13688-024-00465-2","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-024-00465-2","pdf_url":null,"source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPJ Data Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:fa5d18f71cfd4a039b242568cfe4613d","is_oa":false,"landing_page_url":"https://doaj.org/article/fa5d18f71cfd4a039b242568cfe4613d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EPJ Data Science, Vol 13, Iss 1, Pp 1-17 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1140/epjds/s13688-024-00465-2","is_oa":true,"landing_page_url":"https://doi.org/10.1140/epjds/s13688-024-00465-2","pdf_url":null,"source":{"id":"https://openalex.org/S2504380752","display_name":"EPJ Data Science","issn_l":"2193-1127","issn":["2193-1127"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EPJ Data Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5400673922","display_name":null,"funder_award_id":"2022R1F1A1067008","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1967450521","https://openalex.org/W2024932032","https://openalex.org/W2057320572","https://openalex.org/W2250539671","https://openalex.org/W2293755787","https://openalex.org/W2493916176","https://openalex.org/W2535471157","https://openalex.org/W2604272474","https://openalex.org/W2612649659","https://openalex.org/W2908841270","https://openalex.org/W2952214074","https://openalex.org/W2963487003","https://openalex.org/W2963514026","https://openalex.org/W3016827666","https://openalex.org/W3087352798","https://openalex.org/W3135410655","https://openalex.org/W3159716805","https://openalex.org/W3171733996","https://openalex.org/W3183132986","https://openalex.org/W3217459514","https://openalex.org/W4200472682","https://openalex.org/W4243803237","https://openalex.org/W4284693641","https://openalex.org/W4311011809","https://openalex.org/W4389166082","https://openalex.org/W4391585991"],"related_works":["https://openalex.org/W2560936962","https://openalex.org/W2788727012","https://openalex.org/W4388203630","https://openalex.org/W2526386912","https://openalex.org/W1967370444","https://openalex.org/W2150136235","https://openalex.org/W2085215424","https://openalex.org/W2088814244","https://openalex.org/W2157408137","https://openalex.org/W4312252109"],"abstract_inverted_index":{"Abstract":[0],"In":[1],"this":[2],"study,":[3],"we":[4,28,70,97,130],"analyze":[5],"the":[6,19,38,66,72,79,95,118,138,144,156,163,171,175,178,182,201,210],"extent":[7],"of":[8,74,151,177,181,213],"user":[9],"reactions":[10,31,49,93,146],"based":[11,36,208],"on":[12,37,209],"user\u2019s":[13],"tweets":[14,42,105],"to":[15,32,50,94,134,143,187,199],"news":[16,34,44,51,80,122,148,169,179],"articles,":[17,45],"demonstrating":[18],"potential":[20],"for":[21,149],"home":[22,110,152,157,172,183],"location":[23,173,184],"prediction.":[24],"To":[25,64],"achieve":[26],"this,":[27],"quantify":[29],"users\u2019":[30,48,92,202,211],"specific":[33,85],"articles":[35,52,81,123],"textual":[39],"similarity":[40],"between":[41,168],"and":[43,121,174],"showcasing":[46],"that":[47,82,192],"about":[53,61],"their":[54],"cities":[55,116],"are":[56,112],"significantly":[57],"higher":[58],"than":[59],"those":[60,128],"other":[62],"cities.":[63],"maximize":[65],"difference":[67,164],"in":[68,113,127,137,165,170],"reactions,":[69],"introduce":[71],"concept":[73],"News":[75,89,160],"Distinctness,":[76],"which":[77],"highlights":[78],"affect":[83],"a":[84,132],"location.":[86,158],"By":[87],"incorporating":[88],"Distinctness":[90,161],"with":[91,104],"news,":[96],"magnify":[98],"its":[99],"effects.":[100],"Through":[101],"experiments":[102],"conducted":[103],"collected":[106],"from":[107],"users":[108],"whose":[109],"locations":[111],"five":[114],"representative":[115],"within":[117],"United":[119],"States":[120],"describing":[124],"events":[125],"occurring":[126],"cities,":[129],"observed":[131],"6.75%":[133],"40%":[135],"improvement":[136],"reaction":[139,166],"score":[140,167],"when":[141],"compared":[142],"average":[145,176],"towards":[147],"outside":[150,180],"location,":[153,203],"clearly":[154],"predicting":[155],"Furthermore,":[159],"increases":[162],"by":[185],"12%":[186],"194%.":[188],"These":[189],"results":[190],"demonstrate":[191],"our":[193],"proposed":[194],"idea":[195],"can":[196],"be":[197],"utilized":[198],"predict":[200],"potentially":[204],"recommending":[205],"meaningful":[206],"information":[207],"areas":[212],"interest.":[214]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-09-03T10:48:47.769340","created_date":"2025-10-10T00:00:00"}
