{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T04:43:37Z","timestamp":1725684217069},"reference-count":45,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100015963","name":"Scuola IMT Alti Studi Lucca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015963","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing & Management"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1016\/j.ipm.2021.102685","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T21:59:56Z","timestamp":1627423196000},"page":"102685","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":15,"title":["On the efficacy of old features for the detection of new bots"],"prefix":"10.1016","volume":"58","author":[{"given":"Rocco","family":"De Nicola","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0591-877X","authenticated-orcid":false,"given":"Marinella","family":"Petrocchi","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9978-791X","authenticated-orcid":false,"given":"Manuel","family":"Pratelli","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.ipm.2021.102685_b1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/BF00153759","article-title":"Instance-based learning algorithms","volume":"6","author":"Aha","year":"1991","journal-title":"Machine Learning"},{"key":"10.1016\/j.ipm.2021.102685_b2","series-title":"International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation","article-title":"Introducing BotHunter: A tiered approach to detection and characterizing automated activity on Twitter","author":"Beskow","year":"2018"},{"issue":"7","key":"10.1016\/j.ipm.2021.102685_b3","article-title":"Influence of fake news in Twitter during the 2016 US presidential election","volume":"10","author":"Bovet","year":"2019","journal-title":"Nature Communications"},{"issue":"1","key":"10.1016\/j.ipm.2021.102685_b4","doi-asserted-by":"crossref","first-page":"8673","DOI":"10.1038\/s41598-018-26951-y","article-title":"Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump","volume":"8","author":"Bovet","year":"2018","journal-title":"Scientific Reports"},{"issue":"1","key":"10.1016\/j.ipm.2021.102685_b5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.1016\/j.ipm.2021.102685_b6","first-page":"1","article-title":"The role of bot squads in the political propaganda on Twitter","volume":"3","author":"Caldarelli","year":"2019","journal-title":"Communications Physics"},{"issue":"1","key":"10.1016\/j.ipm.2021.102685_b7","first-page":"24","article-title":"Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy","volume":"10","author":"Caldarelli","year":"2021","journal-title":"EPJ Data Science"},{"key":"10.1016\/j.ipm.2021.102685_b8","series-title":"ACM SIGSAC conference on computer and communications security","first-page":"477","article-title":"Uncovering large groups of active malicious accounts in online social networks","author":"Cao","year":"2014"},{"key":"10.1016\/j.ipm.2021.102685_b9","series-title":"2016 IEEE 16th international conference on data mining (ICDM)","first-page":"817","article-title":"Debot: Twitter bot detection via warped correlation","author":"Chavoshi","year":"2016"},{"key":"10.1016\/j.ipm.2021.102685_b10","series-title":"Machine learning proceedings 1995","first-page":"115","article-title":"Fast effective rule induction","author":"Cohen","year":"1995"},{"issue":"10","key":"10.1016\/j.ipm.2021.102685_b11","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1145\/3409116","article-title":"A decade of social bot detection","volume":"63","author":"Cresci","year":"2020","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.ipm.2021.102685_b12","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.dss.2015.09.003","article-title":"Fame for sale: efficient detection of fake Twitter followers","volume":"80","author":"Cresci","year":"2015","journal-title":"Decision Support Systems"},{"issue":"5","key":"10.1016\/j.ipm.2021.102685_b13","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/MIS.2016.29","article-title":"DNA-inspired online behavioral modeling and its application to spambot detection","volume":"31","author":"Cresci","year":"2016","journal-title":"IEEE Intelligent Systems"},{"key":"10.1016\/j.ipm.2021.102685_b14","series-title":"Proceedings of the 26th international conference on world wide web companion (WWW\u201917)","first-page":"963","article-title":"The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race","author":"Cresci","year":"2017"},{"issue":"4","key":"10.1016\/j.ipm.2021.102685_b15","first-page":"561","article-title":"Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling","volume":"15","author":"Cresci","year":"2018","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"issue":"2","key":"10.1016\/j.ipm.2021.102685_b16","doi-asserted-by":"crossref","DOI":"10.1145\/3313184","article-title":"Cashtag piggybacking: Uncovering spam and bot activity in stock microblogs on Twitter","volume":"13","author":"Cresci","year":"2019","journal-title":"ACM Transactions on the Web"},{"issue":"6","key":"10.1016\/j.ipm.2021.102685_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2020.102317","article-title":"Similcatch: Enhanced social spammers detection on Twitter using Markov random fields","volume":"57","author":"El-Mawass","year":"2020","journal-title":"Information Processing & Management"},{"issue":"8","key":"10.1016\/j.ipm.2021.102685_b18","article-title":"Disinformation and social bot operations in the run up to the 2017 French presidential election","volume":"22","author":"Ferrara","year":"2017","journal-title":"First Monday"},{"issue":"7","key":"10.1016\/j.ipm.2021.102685_b19","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1145\/2818717","article-title":"The rise of social bots","volume":"59","author":"Ferrara","year":"2016","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.ipm.2021.102685_b20","series-title":"PAKDD","article-title":"ND-Sync: Detecting synchronized fraud activities","author":"Giatsoglou","year":"2015"},{"key":"10.1016\/j.ipm.2021.102685_b21","series-title":"Proceedings of the fourteenth international AAAI conference on web and social media, ICWSM 2020, held virtually, original venue: Atlanta, Georgia, USA, June 8-11, 2020","first-page":"980","article-title":"Botslayer: DIY real-time influence campaign detection","author":"Hui","year":"2020"},{"issue":"1","key":"10.1016\/j.ipm.2021.102685_b22","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/MIS.2016.5","article-title":"Suspicious behavior detection: Current trends and future directions","volume":"31","author":"Jiang","year":"2016","journal-title":"IEEE Intelligent Systems"},{"key":"10.1016\/j.ipm.2021.102685_b23","series-title":"Proceedings of the eleventh conference on uncertainty in artificial intelligence","first-page":"338","article-title":"Estimating continuous distributions in Bayesian classifiers","author":"John","year":"1995"},{"issue":"1","key":"10.1016\/j.ipm.2021.102685_b24","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1093\/biomet\/70.1.163","article-title":"Information gain and a general measure of correlation","volume":"70","author":"Kent","year":"1983","journal-title":"Biometrika"},{"key":"10.1016\/j.ipm.2021.102685_b25","series-title":"11th ACM Conference on Web Science","first-page":"183","article-title":"RTbust: Exploiting temporal patterns for botnet detection on Twitter","author":"Mazza","year":"2019"},{"issue":"4","key":"10.1016\/j.ipm.2021.102685_b26","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/S0001-2998(78)80014-2","article-title":"Basic principles of ROC analysis","volume":"8","author":"Metz","year":"1978","journal-title":"Seminars in Nuclear Medicine"},{"key":"10.1016\/j.ipm.2021.102685_b27","series-title":"Machine learning","author":"Mitchell","year":"1997"},{"key":"10.1016\/j.ipm.2021.102685_b28","series-title":"Web science: Extending the frontiers of society on-line","article-title":"From obscurity to prominence in minutes: Political speech and real-time search","author":"Mustafaraj","year":"2010"},{"issue":"8","key":"10.1016\/j.ipm.2021.102685_b29","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.jclinepi.2015.02.010","article-title":"The precision\u2013recall curve overcame the optimism of the Receiver Operating Characteristic curve in rare diseases","volume":"68","author":"Ozenne","year":"2015","journal-title":"Journal of Clinical Epidemiology"},{"issue":"5","key":"10.1016\/j.ipm.2021.102685_b30","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1109\/72.159058","article-title":"Multilayer perceptron, fuzzy sets, and classification","volume":"3","author":"Pal","year":"1992","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"85","key":"10.1016\/j.ipm.2021.102685_b31","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"issue":"3","key":"10.1016\/j.ipm.2021.102685_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PLOS ONE"},{"key":"10.1016\/j.ipm.2021.102685_b33","series-title":"CIKM \u201920: The 29th ACM international conference on information and knowledge management, Virtual Event, Ireland, October 19-23, 2020","first-page":"2725","article-title":"Detection of novel social bots by ensembles of specialized classifiers","author":"Sayyadiharikandeh","year":"2020"},{"issue":"1","key":"10.1016\/j.ipm.2021.102685_b34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0244309","article-title":"Insights into elections: An ensemble bot detection coverage framework applied to the 2018 U.S. midterm elections","volume":"16","author":"Schuchard","year":"2021","journal-title":"PLOS ONE"},{"key":"10.1016\/j.ipm.2021.102685_b35","series-title":"Identifying coordinated accounts in disinformation campaigns","author":"Sharma","year":"2020"},{"key":"10.1016\/j.ipm.2021.102685_b36","series-title":"Episode 5: The bits and bots of the web with Fil Menczer","year":"2020"},{"key":"10.1016\/j.ipm.2021.102685_b37","series-title":"Proceedings of the eleventh international conference on web and social media, ICWSM 2017, Montr\u00e9al, Qu\u00e9bec, Canada, May 15-18, 2017","first-page":"280","article-title":"Online human-bot interactions: Detection, estimation, and characterization","author":"Varol","year":"2017"},{"key":"10.1016\/j.ipm.2021.102685_b38","series-title":"La scienza nuova","author":"Vico","year":"1744"},{"key":"10.1016\/j.ipm.2021.102685_b39","series-title":"Proceedings of the 2015 ACM on conference on online social networks","first-page":"113","article-title":"Strength in numbers: Robust tamper detection in crowd computations","author":"Viswanath","year":"2015"},{"key":"10.1016\/j.ipm.2021.102685_b40","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1613\/jair.1199","article-title":"Learning when training data are costly: The effect of class distribution on tree induction","volume":"19","author":"Weiss","year":"2003","journal-title":"Journal of Artificial Intelligence Research"},{"key":"10.1016\/j.ipm.2021.102685_b41","series-title":"Data mining: Practical machine learning tools and techniques","author":"Witten","year":"2011"},{"key":"10.1016\/j.ipm.2021.102685_b42","first-page":"48","article-title":"Arming the public with artificial intelligence to counter social bots","author":"Yang","year":"2020","journal-title":"Human Behavior and Emerging Technologies"},{"key":"10.1016\/j.ipm.2021.102685_b43","first-page":"1096","article-title":"Scalable and generalizable social bot detection through data selection","author":"Yang","year":"2020"},{"issue":"1","key":"10.1016\/j.ipm.2021.102685_b44","article-title":"Detecting spam in a Twitter network","volume":"15","author":"Yardi","year":"2010","journal-title":"First Monday"},{"issue":"2","key":"10.1016\/j.ipm.2021.102685_b45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2811268","article-title":"GLAD: Group anomaly detection in social media analysis","volume":"10","author":"Yu","year":"2015","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"}],"container-title":["Information Processing & Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457321001709?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457321001709?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,1,29]],"date-time":"2023-01-29T10:34:47Z","timestamp":1674988487000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457321001709"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":45,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["S0306457321001709"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.ipm.2021.102685","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"On the efficacy of old features for the detection of new bots","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2021.102685","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"102685"}}
