{"id":"https://openalex.org/W2085733363","doi":"https://doi.org/10.1145/2505515.2505579","title":"Spatio-temporal meme prediction","display_name":"Spatio-temporal meme prediction","publication_year":2013,"publication_date":"2013-10-27","ids":{"openalex":"https://openalex.org/W2085733363","doi":"https://doi.org/10.1145/2505515.2505579","mag":"2085733363"},"language":"en","primary_location":{"id":"doi:10.1145/2505515.2505579","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2505515.2505579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","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/A5114488878","display_name":"Krishna Kamath","orcid":"https://orcid.org/0009-0009-8414-5074"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Krishna Y. Kamath","raw_affiliation_strings":["Texas A&M University, College Station, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048489384","display_name":"James Caverlee","orcid":"https://orcid.org/0000-0001-8350-8528"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Caverlee","raw_affiliation_strings":["Texas A&M University, College Station, TX, USA"],"affiliations":[{"raw_affiliation_string":"Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5114488878"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":3.2396,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.92187646,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1341","last_page":"1350"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.995199978351593,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8120579719543457},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.6304032206535339},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5047656297683716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47658321261405945},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.45009782910346985},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4373128116130829},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43565040826797485},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13024026155471802},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07630214095115662},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0705798864364624}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8120579719543457},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.6304032206535339},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5047656297683716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47658321261405945},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.45009782910346985},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4373128116130829},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43565040826797485},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13024026155471802},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07630214095115662},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0705798864364624},{"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:10.1145/2505515.2505579","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2505515.2505579","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W7143572","https://openalex.org/W1521219253","https://openalex.org/W1599541430","https://openalex.org/W1654194294","https://openalex.org/W1752870744","https://openalex.org/W1973749534","https://openalex.org/W1990128172","https://openalex.org/W1996816151","https://openalex.org/W2018277822","https://openalex.org/W2061820396","https://openalex.org/W2065512234","https://openalex.org/W2080318890","https://openalex.org/W2091802992","https://openalex.org/W2101196063","https://openalex.org/W2102764191","https://openalex.org/W2110299249","https://openalex.org/W2112251034","https://openalex.org/W2114544578","https://openalex.org/W2125349172","https://openalex.org/W2134746982","https://openalex.org/W2140540364","https://openalex.org/W2145446394","https://openalex.org/W2168346693","https://openalex.org/W2949567784","https://openalex.org/W2962860893","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W4391331176","https://openalex.org/W2031695474","https://openalex.org/W2586732548","https://openalex.org/W3025157844"],"abstract_inverted_index":{"In":[0,72],"this":[1],"paper,":[2],"we":[3,19,58,74],"tackle":[4],"the":[5,25,40,68,77],"problem":[6],"of":[7,28,43],"predicting":[8],"what":[9,16],"online":[10,44],"memes":[11],"will":[12,51],"be":[13],"popular":[14,53],"in":[15,54],"locations.":[17,56],"Specifically,":[18],"develop":[20,59],"data-driven":[21],"approaches":[22],"building":[23],"on":[24],"global":[26],"footprint":[27],"755":[29],"million":[30],"geo-tagged":[31],"hashtags":[32,50],"spread":[33,46],"via":[34],"Twitter.":[35],"Our":[36],"proposed":[37,78],"methods":[38],"model":[39],"geo-spatial":[41,70],"propagation":[42],"information":[45],"to":[47],"identify":[48],"which":[49],"become":[52],"specific":[55],"Concretely,":[57],"a":[60],"novel":[61],"reinforcement":[62],"learning":[63],"approach":[64],"that":[65,76],"incrementally":[66],"updates":[67],"best":[69],"model.":[71],"experiments,":[73],"find":[75],"method":[79],"outperforms":[80],"alternative":[81],"linear":[82],"regression":[83],"based":[84],"methods.":[85]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}