{"id":"https://openalex.org/W2016266039","doi":"https://doi.org/10.1145/2487575.2487580","title":"Spotting opinion spammers using behavioral footprints","display_name":"Spotting opinion spammers using behavioral footprints","publication_year":2013,"publication_date":"2013-08-11","ids":{"openalex":"https://openalex.org/W2016266039","doi":"https://doi.org/10.1145/2487575.2487580","mag":"2016266039"},"language":"en","primary_location":{"id":"doi:10.1145/2487575.2487580","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2487575.2487580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5078060919","display_name":"Arjun Mukherjee","orcid":"https://orcid.org/0000-0002-8896-604X"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arjun Mukherjee","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070776545","display_name":"Abhinav Kumar","orcid":"https://orcid.org/0000-0001-9367-7069"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Kumar","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339927","display_name":"Bing Liu","orcid":"https://orcid.org/0000-0002-4096-6980"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bing Liu","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101714278","display_name":"Junhui Wang","orcid":"https://orcid.org/0000-0002-9165-5664"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junhui Wang","raw_affiliation_strings":["University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109925930","display_name":"Meichun Hsu","orcid":null},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meichun Hsu","raw_affiliation_strings":["HP Labs, Palo Alto, CA, USA","HP Labs, Palo ALto, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Labs, Palo Alto, CA, USA","institution_ids":["https://openalex.org/I1324840837"]},{"raw_affiliation_string":"HP Labs, Palo ALto, CA, USA","institution_ids":["https://openalex.org/I1324840837"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110218927","display_name":"Mal\u00fa Castellanos","orcid":null},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Malu Castellanos","raw_affiliation_strings":["HP Labs, Palo Alto, CA, USA","HP Labs, Palo ALto, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Labs, Palo Alto, CA, USA","institution_ids":["https://openalex.org/I1324840837"]},{"raw_affiliation_string":"HP Labs, Palo ALto, CA, USA","institution_ids":["https://openalex.org/I1324840837"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051076553","display_name":"Riddhiman Ghosh","orcid":null},"institutions":[{"id":"https://openalex.org/I1324840837","display_name":"Hewlett-Packard (United States)","ror":"https://ror.org/059rn9488","country_code":"US","type":"company","lineage":["https://openalex.org/I1324840837"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Riddhiman Ghosh","raw_affiliation_strings":["HP Labs, Palo Alto, CA, USA","HP Labs, Palo ALto, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HP Labs, Palo Alto, CA, USA","institution_ids":["https://openalex.org/I1324840837"]},{"raw_affiliation_string":"HP Labs, Palo ALto, CA, USA","institution_ids":["https://openalex.org/I1324840837"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":23.6552,"has_fulltext":false,"cited_by_count":422,"citation_normalized_percentile":{"value":0.99825794,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"632","last_page":"640"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11644","display_name":"Spam and Phishing Detection","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11147","display_name":"Misinformation and Its Impacts","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.6765061616897583},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5954868197441101},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.552066445350647},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5018513202667236},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4965844750404358},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.4512074887752533},{"id":"https://openalex.org/keywords/spotting","display_name":"Spotting","score":0.443093478679657},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.4413508474826813},{"id":"https://openalex.org/keywords/spamming","display_name":"Spamming","score":0.4200045168399811},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4126646816730499},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3675777316093445},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3132287561893463},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.1627383530139923},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.14715364575386047}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6765061616897583},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5954868197441101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.552066445350647},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5018513202667236},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4965844750404358},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.4512074887752533},{"id":"https://openalex.org/C2779506182","wikidata":"https://www.wikidata.org/wiki/Q7580141","display_name":"Spotting","level":2,"score":0.443093478679657},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4413508474826813},{"id":"https://openalex.org/C158955206","wikidata":"https://www.wikidata.org/wiki/Q83058","display_name":"Spamming","level":3,"score":0.4200045168399811},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4126646816730499},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3675777316093445},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3132287561893463},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.1627383530139923},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.14715364575386047},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2487575.2487580","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2487575.2487580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.423.1899","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.423.1899","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.uic.edu/~liub/publications/KDD-2013-Arjun-spam.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8100000023841858}],"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":47,"referenced_works":["https://openalex.org/W60106149","https://openalex.org/W106005634","https://openalex.org/W893486657","https://openalex.org/W1506806321","https://openalex.org/W1574862351","https://openalex.org/W1585743408","https://openalex.org/W1663973292","https://openalex.org/W1775665607","https://openalex.org/W1877442813","https://openalex.org/W1967495422","https://openalex.org/W1969486090","https://openalex.org/W1973435495","https://openalex.org/W1975879668","https://openalex.org/W2005126631","https://openalex.org/W2005556331","https://openalex.org/W2047221353","https://openalex.org/W2047756776","https://openalex.org/W2050341350","https://openalex.org/W2059640750","https://openalex.org/W2061809273","https://openalex.org/W2073562093","https://openalex.org/W2075827992","https://openalex.org/W2089124807","https://openalex.org/W2091034860","https://openalex.org/W2098062695","https://openalex.org/W2100738695","https://openalex.org/W2103063352","https://openalex.org/W2105689297","https://openalex.org/W2112213600","https://openalex.org/W2118942057","https://openalex.org/W2124637344","https://openalex.org/W2136710010","https://openalex.org/W2148123869","https://openalex.org/W2149427297","https://openalex.org/W2149684865","https://openalex.org/W2159359879","https://openalex.org/W2161283199","https://openalex.org/W2164777277","https://openalex.org/W2168479832","https://openalex.org/W2170907675","https://openalex.org/W2180101149","https://openalex.org/W2189187207","https://openalex.org/W2202307757","https://openalex.org/W2402329314","https://openalex.org/W3123554940","https://openalex.org/W4285719527","https://openalex.org/W6629510986"],"related_works":["https://openalex.org/W1987732684","https://openalex.org/W2286465138","https://openalex.org/W637393809","https://openalex.org/W2808742736","https://openalex.org/W3126526144","https://openalex.org/W1526983901","https://openalex.org/W3021299372","https://openalex.org/W26704839","https://openalex.org/W2796920963","https://openalex.org/W2891616219"],"abstract_inverted_index":{"Opinionated":[0],"social":[1],"media":[2],"such":[3],"as":[4,99,123],"product":[5],"reviews":[6],"are":[7,180],"now":[8],"widely":[9],"used":[10],"by":[11,34,96],"individuals":[12],"and":[13,63,78,125,162],"organizations":[14],"for":[15,75],"their":[16],"decision":[17],"making.":[18],"However,":[19,66],"due":[20,67],"to":[21,30,41,44,68,83,93,128],"the":[22,32,61,69,80,94,114,154,169,173,195,198,204],"reason":[23],"of":[24,71,121,134,158,172,178,197],"profit":[25],"or":[26,43],"fame,":[27],"people":[28],"try":[29],"game":[31],"system":[33],"opinion":[35,140],"spamming":[36],"(e.g.,":[37],"writing":[38],"fake":[39,52],"reviews)":[40],"promote":[42],"demote":[45],"some":[46],"target":[47],"products.":[48],"In":[49],"recent":[50],"years,":[51],"review":[53,192],"detection":[54],"has":[55],"attracted":[56],"significant":[57],"attention":[58],"from":[59,184],"both":[60],"business":[62],"research":[64],"communities.":[65],"difficulty":[70],"human":[72],"labeling":[73],"needed":[74],"supervised":[76],"learning":[77,168],"evaluation,":[79],"problem":[81,95],"remains":[82],"be":[84],"highly":[85],"challenging.":[86],"This":[87,148],"work":[88],"proposes":[89],"a":[90,150,189],"novel":[91],"angle":[92],"modeling":[97,119],"spamicity":[98,120],"latent.":[100],"An":[101],"unsupervised":[102],"model,":[103],"called":[104],"Author":[105],"Spamicity":[106],"Model":[107,164],"(ASM),":[108],"is":[109,138],"proposed.":[110],"It":[111],"works":[112],"in":[113,167],"Bayesian":[115],"setting,":[116],"which":[117,201],"facilitates":[118],"authors":[122],"latent":[124,155],"allows":[126],"us":[127],"exploit":[129],"various":[130],"observed":[131],"behavioral":[132,144],"footprints":[133],"reviewers.":[135],"The":[136],"intuition":[137],"that":[139],"spammers":[141,161],"have":[142],"different":[143,185],"distributions":[145,157,171],"than":[146],"non-spammers.":[147,163],"creates":[149],"distributional":[151],"divergence":[152],"between":[153],"population":[156,170],"two":[159,174],"clusters:":[160],"inference":[165],"results":[166],"clusters.":[175],"Several":[176],"extensions":[177],"ASM":[179],"also":[181],"considered":[182],"leveraging":[183],"priors.":[186],"Experiments":[187],"on":[188],"real-life":[190],"Amazon":[191],"dataset":[193],"demonstrate":[194],"effectiveness":[196],"proposed":[199],"models":[200],"significantly":[202],"outperform":[203],"state-of-the-art":[205],"competitors.":[206]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":40},{"year":2020,"cited_by_count":64},{"year":2019,"cited_by_count":46},{"year":2018,"cited_by_count":56},{"year":2017,"cited_by_count":47},{"year":2016,"cited_by_count":36},{"year":2015,"cited_by_count":40},{"year":2014,"cited_by_count":17},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
