{"id":"https://openalex.org/W2169718890","doi":"https://doi.org/10.1109/bigdata.2015.7363842","title":"America Tweets China: A fine-grained analysis of the state and individual characteristics regarding attitudes towards China","display_name":"America Tweets China: A fine-grained analysis of the state and individual characteristics regarding attitudes towards China","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2169718890","doi":"https://doi.org/10.1109/bigdata.2015.7363842","mag":"2169718890"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2015.7363842","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363842","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 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/A5110436959","display_name":"Yu Wang","orcid":"https://orcid.org/0009-0003-4018-7439"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yu Wang","raw_affiliation_strings":["University of Rochester, Rochester, NY, US"],"affiliations":[{"raw_affiliation_string":"University of Rochester, Rochester, NY, US","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004652202","display_name":"Jianbo Yuan","orcid":"https://orcid.org/0000-0001-7949-9841"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianbo Yuan","raw_affiliation_strings":["Department of Political Science, University of Rochester, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Political Science, University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055469774","display_name":"Jiebo Luo","orcid":"https://orcid.org/0000-0002-4516-9729"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiebo Luo","raw_affiliation_strings":["Department of Political Science, University of Rochester, Rochester, NY, USA"],"affiliations":[{"raw_affiliation_string":"Department of Political Science, University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110436959"],"corresponding_institution_ids":["https://openalex.org/I5388228"],"apc_list":null,"apc_paid":null,"fwci":0.7158,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.71636104,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"936","last_page":"943"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9962999820709229,"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":0.9962999820709229,"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/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9955999851226807,"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":0.9821000099182129,"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/china","display_name":"China","score":0.8685402274131775},{"id":"https://openalex.org/keywords/stylized-fact","display_name":"Stylized fact","score":0.7659398913383484},{"id":"https://openalex.org/keywords/homogeneous","display_name":"Homogeneous","score":0.5985594391822815},{"id":"https://openalex.org/keywords/public-opinion","display_name":"Public opinion","score":0.5274693369865417},{"id":"https://openalex.org/keywords/replicate","display_name":"Replicate","score":0.5017940998077393},{"id":"https://openalex.org/keywords/opinion-poll","display_name":"Opinion poll","score":0.4821993410587311},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.47014787793159485},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.43752753734588623},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.43484702706336975},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4340868890285492},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.41045740246772766},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.40131449699401855},{"id":"https://openalex.org/keywords/demographic-economics","display_name":"Demographic economics","score":0.36668676137924194},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34223678708076477},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22527828812599182},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.221588134765625},{"id":"https://openalex.org/keywords/law","display_name":"Law","score":0.18628725409507751},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17332452535629272},{"id":"https://openalex.org/keywords/politics","display_name":"Politics","score":0.15158525109291077}],"concepts":[{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.8685402274131775},{"id":"https://openalex.org/C38935604","wikidata":"https://www.wikidata.org/wiki/Q4330363","display_name":"Stylized fact","level":2,"score":0.7659398913383484},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.5985594391822815},{"id":"https://openalex.org/C134698397","wikidata":"https://www.wikidata.org/wiki/Q17946","display_name":"Public opinion","level":3,"score":0.5274693369865417},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.5017940998077393},{"id":"https://openalex.org/C2779509655","wikidata":"https://www.wikidata.org/wiki/Q49958","display_name":"Opinion poll","level":4,"score":0.4821993410587311},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.47014787793159485},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.43752753734588623},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.43484702706336975},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4340868890285492},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.41045740246772766},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.40131449699401855},{"id":"https://openalex.org/C4249254","wikidata":"https://www.wikidata.org/wiki/Q3044431","display_name":"Demographic economics","level":1,"score":0.36668676137924194},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34223678708076477},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22527828812599182},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.221588134765625},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.18628725409507751},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17332452535629272},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.15158525109291077},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2015.7363842","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2015.7363842","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338335","display_name":"H2020 European Research Council","ror":"https://ror.org/0472cxd90"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W201361503","https://openalex.org/W1590495275","https://openalex.org/W2005617635","https://openalex.org/W2089486804","https://openalex.org/W2109312871","https://openalex.org/W2112251034","https://openalex.org/W2112332950","https://openalex.org/W2142344524","https://openalex.org/W2151378814","https://openalex.org/W2161834943","https://openalex.org/W2171468534","https://openalex.org/W2186498539","https://openalex.org/W2574024151","https://openalex.org/W4299378949","https://openalex.org/W4300375087","https://openalex.org/W6608276657","https://openalex.org/W6676867598","https://openalex.org/W6686566485"],"related_works":["https://openalex.org/W568485713","https://openalex.org/W3110227482","https://openalex.org/W4383982064","https://openalex.org/W999808306","https://openalex.org/W3200431696","https://openalex.org/W2621023371","https://openalex.org/W2051989254","https://openalex.org/W1771353029","https://openalex.org/W2330298727","https://openalex.org/W2385924171"],"abstract_inverted_index":{"The":[0,85],"U.S.-China":[1,38],"relationship":[2,9],"is":[3,16,66,143],"arguably":[4],"the":[5,11,46,123,147,160,172,188],"most":[6,47,74,175,185],"important":[7],"bilateral":[8],"in":[10,136],"21st":[12],"century.":[13],"Typically":[14],"it":[15],"measured":[17],"through":[18],"opinion":[19,54,70,104,151],"polls,":[20,55],"for":[21,114,133],"example,":[22],"by":[23],"Gallup":[24],"and":[25,117,127,130,169,182,204],"Pew":[26],"Institute.":[27],"In":[28],"this":[29],"paper,":[30],"we":[31,163,191],"propose":[32],"a":[33,75],"new":[34,157],"method":[35,57,111],"to":[36,94,145],"measure":[37],"relations":[39],"using":[40],"data":[41,80,90],"from":[42,150],"Twitter,":[43],"one":[44],"of":[45,88,125,211],"popular":[48],"social":[49],"networks.":[50],"Compared":[51],"with":[52],"traditional":[53],"our":[56,63,89,110,137,141],"has":[58,82],"two":[59],"distinctive":[60],"advantages.":[61],"First,":[62],"sample":[64],"size":[65,87],"significantly":[67],"larger.":[68],"National":[69],"polls":[71,105,152],"have":[72,106],"at":[73],"few":[76],"thousand":[77],"samples.":[78,84],"Our":[79],"set":[81,91],"724,146":[83],"large":[86,103],"enables":[92],"us":[93],"perform":[95],"state":[96,116,161],"level":[97],"analysis,":[98],"which":[99],"so":[100],"far":[101],"even":[102],"left":[107],"unexplored.":[108],"Second,":[109],"can":[112],"control":[113,132],"fixed":[115],"date":[118],"effects.":[119],"We":[120,207],"first":[121],"demonstrate":[122],"existence":[124],"inter-state":[126],"inter-day":[128],"variances":[129,135],"then":[131],"these":[134],"regression":[138],"analysis.":[139],"Empirically,":[140],"study":[142],"able":[144],"replicate":[146],"stylized":[148],"results":[149],"as":[153,155,197],"well":[154],"generate":[156],"insights.":[158],"At":[159,187],"level,":[162,190],"find":[164,192,209],"New":[165],"York,":[166],"Michigan,":[167],"Indiana":[168],"Arizona":[170],"are":[171,184,214],"top":[173],"four":[174],"China-friendly":[176],"states.":[177],"Wyoming,":[178],"South":[179],"Dakota,":[180],"Kansas":[181],"Nevada":[183],"homogeneous.":[186],"individual":[189],"attitudes":[193],"towards":[194],"China":[195],"improve":[196],"an":[198],"individual's":[199],"Twitter":[200],"experience":[201],"grows":[202],"longer":[203],"more":[205,216],"intense.":[206],"also":[208],"individuals":[210],"Chinese":[212],"ethnicity":[213],"statistically":[215],"China-friendly.":[217]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
