{"id":"https://openalex.org/W3006357701","doi":"https://doi.org/10.1109/uemcon47517.2019.8993082","title":"Comparison of Machine Learning Models to Predict Twitter Buzz","display_name":"Comparison of Machine Learning Models to Predict Twitter Buzz","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3006357701","doi":"https://doi.org/10.1109/uemcon47517.2019.8993082","mag":"3006357701"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon47517.2019.8993082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8993082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","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/A5090315822","display_name":"Yash Parikh","orcid":"https://orcid.org/0000-0002-6797-7911"},"institutions":[{"id":"https://openalex.org/I171851574","display_name":"Ramapo College","ror":"https://ror.org/03s2x9d90","country_code":"US","type":"education","lineage":["https://openalex.org/I171851574"]},{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]},{"id":"https://openalex.org/I64281891","display_name":"College of New Jersey","ror":"https://ror.org/02nx5r318","country_code":"US","type":"education","lineage":["https://openalex.org/I64281891"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yash Parikh","raw_affiliation_strings":["School of Theoretical & Applied Science, Ramapo College of New Jersey, Mahwah, NJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Theoretical & Applied Science, Ramapo College of New Jersey, Mahwah, NJ","institution_ids":["https://openalex.org/I171851574","https://openalex.org/I64281891","https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003980841","display_name":"Eman Abdelfattah","orcid":"https://orcid.org/0000-0002-5184-0339"},"institutions":[{"id":"https://openalex.org/I171851574","display_name":"Ramapo College","ror":"https://ror.org/03s2x9d90","country_code":"US","type":"education","lineage":["https://openalex.org/I171851574"]},{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]},{"id":"https://openalex.org/I64281891","display_name":"College of New Jersey","ror":"https://ror.org/02nx5r318","country_code":"US","type":"education","lineage":["https://openalex.org/I64281891"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eman Abdelfattah","raw_affiliation_strings":["School of Theoretical & Applied Science, Ramapo College of New Jersey, Mahwah, NJ"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Theoretical & Applied Science, Ramapo College of New Jersey, Mahwah, NJ","institution_ids":["https://openalex.org/I171851574","https://openalex.org/I64281891","https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19403134,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"0069","last_page":"0073"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9986000061035156,"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":0.9986000061035156,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9951000213623047,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9941999912261963,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/marketing-buzz","display_name":"Marketing buzz","score":0.9654610753059387},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.766532838344574},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6974424719810486},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6965192556381226},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6790828704833984},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6454108953475952},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.62174391746521},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5245728492736816},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.515900731086731},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.4217734634876251},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.10884532332420349}],"concepts":[{"id":"https://openalex.org/C113993141","wikidata":"https://www.wikidata.org/wiki/Q906759","display_name":"Marketing buzz","level":2,"score":0.9654610753059387},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.766532838344574},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6974424719810486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6965192556381226},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6790828704833984},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6454108953475952},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.62174391746521},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5245728492736816},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.515900731086731},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4217734634876251},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.10884532332420349}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uemcon47517.2019.8993082","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8993082","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.5}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W170896097","https://openalex.org/W2015186536","https://openalex.org/W2026318959","https://openalex.org/W2032342806","https://openalex.org/W2114544578","https://openalex.org/W2117239687","https://openalex.org/W2146341589","https://openalex.org/W2265862919","https://openalex.org/W6606940212","https://openalex.org/W6693370502"],"related_works":["https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W4224941037","https://openalex.org/W2004826645","https://openalex.org/W4206903459","https://openalex.org/W2754816816","https://openalex.org/W4366280654","https://openalex.org/W3160167280","https://openalex.org/W4231621013"],"abstract_inverted_index":{"This":[0,42],"paper":[1],"investigates":[2],"six":[3],"machine-learning":[4],"models":[5],"to":[6],"determine":[7],"which":[8],"algorithm":[9],"would":[10],"effectively":[11],"predict":[12],"buzz":[13],"on":[14,38,58],"Twitter.":[15],"Different":[16],"classifiers":[17],"are":[18],"applied":[19],"such":[20],"as":[21],"Stochastic":[22],"Gradient":[23],"Descent,":[24],"Support":[25],"Vector":[26],"Machines,":[27],"Logistic":[28],"Regression,":[29],"Deep":[30],"Neural":[31],"Networks,":[32],"Random":[33],"Forests":[34],"and":[35,48],"Extra":[36,65],"Trees":[37,66],"a":[39,52],"Twitter":[40],"dataset.":[41],"dataset":[43],"contains":[44],"features":[45],"with":[46],"users":[47],"author":[49],"engagement":[50],"over":[51],"certain":[53],"period.":[54],"After":[55],"tests":[56],"conducted":[57],"all":[59],"the":[60,69],"algorithms,":[61],"we":[62],"concluded":[63],"that":[64],"model":[67],"outperforms":[68],"other":[70],"models.":[71]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}