{"id":"https://openalex.org/W4410267997","doi":"https://doi.org/10.1007/s13278-025-01464-5","title":"Two-stage classifier for detecting campaign negativity with axis embeddings in persian tweets","display_name":"Two-stage classifier for detecting campaign negativity with axis embeddings in persian tweets","publication_year":2025,"publication_date":"2025-05-11","ids":{"openalex":"https://openalex.org/W4410267997","doi":"https://doi.org/10.1007/s13278-025-01464-5"},"language":"en","primary_location":{"id":"doi:10.1007/s13278-025-01464-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s13278-025-01464-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s13278-025-01464-5.pdf","source":{"id":"https://openalex.org/S2764891196","display_name":"Social Network Analysis and Mining","issn_l":"1869-5450","issn":["1869-5450","1869-5469"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Social Network Analysis and Mining","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s13278-025-01464-5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041167076","display_name":"Fatemeh Rajabi","orcid":"https://orcid.org/0000-0001-9759-9109"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Fatemeh Rajabi","raw_affiliation_strings":["Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027094623","display_name":"Ali Mohades","orcid":"https://orcid.org/0000-0002-6118-2245"},"institutions":[{"id":"https://openalex.org/I158248296","display_name":"Amirkabir University of Technology","ror":"https://ror.org/04gzbav43","country_code":"IR","type":"education","lineage":["https://openalex.org/I158248296"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Ali Mohades","raw_affiliation_strings":["Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran","institution_ids":["https://openalex.org/I158248296"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5041167076"],"corresponding_institution_ids":["https://openalex.org/I158248296"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04433129,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9987000226974487,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.9952999949455261,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9933000206947327,"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/computer-science","display_name":"Computer science","score":0.575463593006134},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5497369170188904},{"id":"https://openalex.org/keywords/negativity-effect","display_name":"Negativity effect","score":0.5437697768211365},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5350196361541748},{"id":"https://openalex.org/keywords/persian","display_name":"Persian","score":0.5094967484474182},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.4190293252468109},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.395020455121994},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3483952581882477},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.13807862997055054},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10540884733200073}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.575463593006134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5497369170188904},{"id":"https://openalex.org/C7453019","wikidata":"https://www.wikidata.org/wiki/Q16254302","display_name":"Negativity effect","level":2,"score":0.5437697768211365},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5350196361541748},{"id":"https://openalex.org/C2776527531","wikidata":"https://www.wikidata.org/wiki/Q9168","display_name":"Persian","level":2,"score":0.5094967484474182},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.4190293252468109},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.395020455121994},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3483952581882477},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13807862997055054},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10540884733200073},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s13278-025-01464-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s13278-025-01464-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s13278-025-01464-5.pdf","source":{"id":"https://openalex.org/S2764891196","display_name":"Social Network Analysis and Mining","issn_l":"1869-5450","issn":["1869-5450","1869-5469"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Social Network Analysis and Mining","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s13278-025-01464-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s13278-025-01464-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s13278-025-01464-5.pdf","source":{"id":"https://openalex.org/S2764891196","display_name":"Social Network Analysis and Mining","issn_l":"1869-5450","issn":["1869-5450","1869-5469"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","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":"Social Network Analysis and Mining","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410267997.pdf","grobid_xml":"https://content.openalex.org/works/W4410267997.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1995863888","https://openalex.org/W2000580984","https://openalex.org/W2195980099","https://openalex.org/W2513151056","https://openalex.org/W2759242272","https://openalex.org/W2803414046","https://openalex.org/W2898167544","https://openalex.org/W2903227234","https://openalex.org/W2937423263","https://openalex.org/W2942002588","https://openalex.org/W2952638691","https://openalex.org/W2963094815","https://openalex.org/W2964230347","https://openalex.org/W2989902081","https://openalex.org/W3002694930","https://openalex.org/W3013926883","https://openalex.org/W3026044702","https://openalex.org/W3032299950","https://openalex.org/W3082031608","https://openalex.org/W3083527349","https://openalex.org/W3084074548","https://openalex.org/W3091300912","https://openalex.org/W3105625590","https://openalex.org/W3109782035","https://openalex.org/W3136875565","https://openalex.org/W3156333129","https://openalex.org/W3177130671","https://openalex.org/W3212972574","https://openalex.org/W4200608987","https://openalex.org/W4280612849","https://openalex.org/W4281809109","https://openalex.org/W4289296842","https://openalex.org/W4300063949","https://openalex.org/W4310494058","https://openalex.org/W4386086314"],"related_works":["https://openalex.org/W4232962587","https://openalex.org/W2339787954","https://openalex.org/W2258261728","https://openalex.org/W2330233494","https://openalex.org/W1501405543","https://openalex.org/W2890674960","https://openalex.org/W1888026538","https://openalex.org/W4207067687","https://openalex.org/W2886416464","https://openalex.org/W2262473184"],"abstract_inverted_index":{"Abstract":[0],"In":[1,55],"elections":[2],"worldwide,":[3],"candidates":[4,90],"often":[5],"resort":[6],"to":[7,11,51,104,145,168,204],"negative":[8,138],"campaigning":[9],"due":[10],"pressure":[12],"and":[13,91,94,130,137,160,196],"the":[14,19,36,74,100,116,124,147,172,190],"fear":[15],"of":[16,21,39,76,135,174,192],"failure.":[17],"With":[18],"rise":[20],"social":[22],"media":[23],"platforms":[24],"like":[25],"Twitter,":[26],"political":[27,87,193],"discussions":[28],"are":[29,49,142],"now":[30],"more":[31],"accessible":[32],"than":[33],"ever.":[34],"Given":[35],"vast":[37],"amount":[38],"data":[40],"generated,":[41],"automated":[42],"systems":[43],"for":[44,63,119],"detecting":[45,64],"negativity":[46,65],"in":[47,66,99,199],"campaigns":[48,67],"crucial":[50],"understanding":[52],"candidate":[53],"strategies.":[54],"this":[56,166],"paper,":[57],"we":[58],"propose":[59],"a":[60,69,155,179],"hybrid":[61,148],"model":[62,110,152,167],"using":[68],"two-stage":[70],"classifier":[71],"that":[72,178],"leverages":[73],"strengths":[75],"two":[77,113,120],"machine":[78],"learning":[79],"models.":[80],"We":[81],"collected":[82],"Persian":[83],"tweets":[84,97,170,200],"from":[85,115],"50":[86],"users,":[88],"including":[89],"government":[92],"officials,":[93],"annotated":[95],"5,100":[96],"published":[98],"year":[101],"leading":[102],"up":[103],"Iran\u2019s":[105],"2021":[106],"presidential":[107],"election.":[108],"Our":[109,150],"first":[111],"creates":[112],"datasets":[114,141],"training":[117],"set":[118],"classifiers":[121],"by":[122],"calculating":[123],"cosine":[125],"similarity":[126],"between":[127],"tweet":[128,181],"embeddings":[129,132],"axis":[131],"(the":[133],"average":[134],"positive":[136],"embeddings).":[139],"These":[140],"then":[143],"used":[144],"train":[146],"model.":[149],"best-performing":[151],"(RF-RF)":[153],"achieved":[154],"79%":[156],"macro":[157],"F1":[158,163],"score":[159],"82%":[161],"weighted":[162],"score.":[164],"Applying":[165],"additional":[169],"with":[171],"help":[173],"statistical":[175],"models,":[176],"revealed":[177],"candidate\u2019s":[180],"publication":[182],"timing":[183],"does":[184],"not":[185],"affect":[186],"its":[187],"negativity.":[188,205],"Still,":[189],"presence":[191],"person":[194],"names":[195,198],"organization":[197],"is":[201],"closely":[202],"linked":[203]},"counts_by_year":[],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}
