{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:13:44Z","timestamp":1780589624916,"version":"3.54.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008675","name":"Zayed University","doi-asserted-by":"publisher","award":["R20132"],"award-info":[{"award-number":["R20132"]}],"id":[{"id":"10.13039\/501100008675","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, na\u00efve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features:<jats:italic>favorites_count<\/jats:italic>,<jats:italic>verified<\/jats:italic>,<jats:italic>statuses_count<\/jats:italic>, and<jats:italic>average_tweets_per_day,<\/jats:italic>achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).<\/jats:p>","DOI":"10.1007\/s13278-021-00786-4","type":"journal-article","created":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T08:02:25Z","timestamp":1632038545000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Hybrid feature selection approach to identify optimal features of profile metadata to detect social bots in Twitter"],"prefix":"10.1007","volume":"11","author":[{"given":"Eiman","family":"Alothali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kadhim","family":"Hayawi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5721-5104","authenticated-orcid":false,"given":"Hany","family":"Alashwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,19]]},"reference":[{"key":"786_CR1","doi-asserted-by":"publisher","unstructured":"Abokhodair, N, Daisy Y, McDonald DW (2015) Dissecting a social botnet. In: Proceedings of the 18th ACM conference on computer supported cooperative work and social computing, New York, NY, USA: ACM, 839\u201351. https:\/\/doi.org\/10.1145\/2675133.2675208","DOI":"10.1145\/2675133.2675208"},{"key":"786_CR2","doi-asserted-by":"publisher","unstructured":"Alothali E, Nazar Z, Mohamed EA, Hany A (2018) Detecting social bots on Twitter: a literature review. In: 2018 international conference on innovations in information technology (IIT), IEEE, 175\u201380. https:\/\/doi.org\/10.1109\/INNOVATIONS.2018.8605995","DOI":"10.1109\/INNOVATIONS.2018.8605995"},{"key":"786_CR3","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.ijinfomgt.2018.08.006","volume":"45","author":"H Ariyaluran","year":"2019","unstructured":"H Ariyaluran A Riyaz N Fariza G Abdullah IAT Hashem A Ejaz I Muhammad 2019 Real-time big data processing for anomaly detection: a survey Int J Inf Manag 45 289 307 https:\/\/doi.org\/10.1016\/j.ijinfomgt.2018.08.006","journal-title":"Int J Inf Manag"},{"issue":"1","key":"786_CR4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/s10588-018-09290-1","volume":"25","author":"DM Beskow","year":"2019","unstructured":"DM Beskow KM Carley 2019 Its all in a name: detecting and labeling bots by their name Comput Math Organ Theory 25 1 24 35 https:\/\/doi.org\/10.1007\/s10588-018-09290-1","journal-title":"Comput Math Organ Theory"},{"key":"786_CR5","unstructured":"Botometer (2020) Datasets 2020. https:\/\/botometer.osome.iu.edu\/bot-repository\/datasets.html"},{"key":"786_CR6","doi-asserted-by":"publisher","unstructured":"Cai C, Linjing L, Daniel Z (2017) Behavior enhanced deep bot detection in social media. In: 2017 IEEE International conference on intelligence and security informatics (ISI), IEEE, 128\u201330. https:\/\/doi.org\/10.1109\/ISI.2017.8004887.","DOI":"10.1109\/ISI.2017.8004887"},{"key":"786_CR7","doi-asserted-by":"publisher","unstructured":"Cresci, S, Di Pietro R, Marinella P, Angelo S, Maurizio T (2017) The Paradigm-shift of social spambots. In: Proceedings of the 26th international conference on world wide web companion\u2014WWW \u201917 companion, New York, New York, USA: ACM Press, 963\u201372. https:\/\/doi.org\/10.1145\/3041021.3055135","DOI":"10.1145\/3041021.3055135"},{"key":"786_CR8","doi-asserted-by":"publisher","unstructured":"Cresci S, Marinella P, Angelo S, Stefano T (2019) Better safe than sorry. In: Proceedings of the 10th ACM conference on web science\u2014WebSci. New York, New York, USA: ACM Press, 19:47\u201356. https:\/\/doi.org\/10.1145\/3292522.3326030","DOI":"10.1145\/3292522.3326030"},{"key":"786_CR9","doi-asserted-by":"publisher","first-page":"107175","DOI":"10.1016\/j.asoc.2021.107175","volume":"104","author":"S Dadkhah","year":"2021","unstructured":"S Dadkhah S Farzaneh MM Yadollahi Z Xichen AG Ali 2021 A real-time hostile activities analyses and detection system Appl Soft Comput 104 107175 https:\/\/doi.org\/10.1016\/j.asoc.2021.107175","journal-title":"Appl Soft Comput"},{"key":"786_CR10","doi-asserted-by":"publisher","unstructured":"Devi SG, Sabrigiriraj M (2018) Feature selection, online feature selection techniques for big data classification: a review. In: 2018 international conference on current trends towards converging technologies (ICCTCT), IEEE, 1\u20139. https:\/\/doi.org\/10.1109\/ICCTCT.2018.8550928.","DOI":"10.1109\/ICCTCT.2018.8550928"},{"issue":"7","key":"786_CR11","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1145\/2818717","volume":"59","author":"E Ferrara","year":"2016","unstructured":"E Ferrara O Varol C Davis F Menczer A Flammini 2016 The rise of social bots Commun ACM 59 7 96 104 https:\/\/doi.org\/10.1145\/2818717","journal-title":"Commun ACM"},{"issue":"1","key":"786_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298789","volume":"13","author":"Z Gilani","year":"2019","unstructured":"Z Gilani R Farahbakhsh G Tyson J Crowcroft 2019 A large-scale behavioural analysis of bots and humans on Twitter ACM Trans Web 13 1 1 23 https:\/\/doi.org\/10.1145\/3298789","journal-title":"ACM Trans Web"},{"key":"786_CR13","doi-asserted-by":"publisher","unstructured":"Gilani Z, Liang W, Jon C, Mario A, Reza F (2016) Stweeler: a framework for Twitter bot analysis. In: Proceedings of the 25th international conference companion on world wide web\u2014WWW \u201916 companion, New York, New York, USA: ACM Press, 37\u201338. https:\/\/doi.org\/10.1145\/2872518.2889360","DOI":"10.1145\/2872518.2889360"},{"key":"786_CR14","doi-asserted-by":"publisher","unstructured":"Gilani, Z, Reza F, Gareth T, Liang W, Jon C (2017) Of bots and humans (on Twitter). In: Proceedings of the 2017 IEEE\/ACM international conference on advances in social networks analysis and mining 2017, New York, NY, USA: ACM, 349\u201354. https:\/\/doi.org\/10.1145\/3110025.3110090","DOI":"10.1145\/3110025.3110090"},{"key":"786_CR15","doi-asserted-by":"publisher","unstructured":"Grier C, Kurt T, Vern P, Michael Z (2010) @spam. In: Proceedings of the 17th ACM conference on computer and communications security\u2014CCS \u201910, 27. New York, New York, USA: ACM Press. https:\/\/doi.org\/10.1145\/1866307.1866311","DOI":"10.1145\/1866307.1866311"},{"key":"786_CR16","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(null):1157\u20131182"},{"key":"786_CR17","unstructured":"Hall, MA (2000) Correlation-based feature selection of discrete and numeric class machine learning"},{"issue":"2","key":"786_CR18","first-page":"271","volume":"2","author":"AG Karegowda","year":"2010","unstructured":"AG Karegowda AS Manjunath MA Jayaram 2010 Comparative study of attribute selection using gain ratio and correlation based feature selection Int J Inf Technol Knowl Manag 2 2 271 277","journal-title":"Int J Inf Technol Knowl Manag"},{"issue":"6","key":"786_CR19","doi-asserted-by":"publisher","first-page":"198","DOI":"10.18178\/ijmlc.2017.7.6.646","volume":"7","author":"A Khalil","year":"2017","unstructured":"A Khalil H Hassan N Al-Qirim 2017 Detecting fake followers in Twitter a machine learning approach Int J Mach Learn Comput 7 6 198 202 https:\/\/doi.org\/10.18178\/ijmlc.2017.7.6.646","journal-title":"Int J Mach Learn Comput"},{"key":"786_CR20","doi-asserted-by":"publisher","unstructured":"Khalil H, Muhammad USK, Mazhar A (2020) Feature selection for unsupervised bot detection. In: 2020 3rd international conference on computing, mathematics and engineering technologies (ICoMET), abs\/1703.0:1\u20137. IEEE. https:\/\/doi.org\/10.1109\/iCoMET48670.2020.9074131.","DOI":"10.1109\/iCoMET48670.2020.9074131"},{"key":"786_CR21","doi-asserted-by":"publisher","unstructured":"Kohavi R, George HJ (1997) Wrappers for feature subset selection. Artif Intel 97(1):273\u2013324. https:\/\/doi.org\/10.1016\/S0004-3702(97)00043-X","DOI":"10.1016\/S0004-3702(97)00043-X"},{"key":"786_CR22","doi-asserted-by":"publisher","unstructured":"Kondor D, Istvan C, Laszlo D, Janos S, Norbert B, Tamas H, Tamas S, Zsofia K, Gabor V (2013) Using robust PCA to estimate regional characteristics of language use from geo-tagged Twitter messages. In: 2013 IEEE 4th international conference on cognitive infocommunications (CogInfoCom), IEEE, 393\u201398. https:\/\/doi.org\/10.1109\/CogInfoCom.2013.6719277","DOI":"10.1109\/CogInfoCom.2013.6719277"},{"key":"786_CR23","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.ins.2018.08.019","volume":"467","author":"S Kudugunta","year":"2018","unstructured":"S Kudugunta E Ferrara 2018 Deep neural networks for bot detection Inf Sci 467 312 322 https:\/\/doi.org\/10.1016\/j.ins.2018.08.019","journal-title":"Inf Sci"},{"issue":"2","key":"786_CR24","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1109\/MIS.2017.38","volume":"32","author":"J Li","year":"2017","unstructured":"J Li H Liu 2017 Challenges of feature selection for big data analytics IEEE Intell Syst 32 2 9 15 https:\/\/doi.org\/10.1109\/MIS.2017.38","journal-title":"IEEE Intell Syst"},{"issue":"13","key":"786_CR25","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.1016\/j.ins.2009.02.014","volume":"179","author":"S Maldonado","year":"2009","unstructured":"S Maldonado R Weber 2009 A wrapper method for feature selection using support vector machines Inf Sci 179 13 2208 2217 https:\/\/doi.org\/10.1016\/j.ins.2009.02.014","journal-title":"Inf Sci"},{"key":"786_CR26","doi-asserted-by":"publisher","unstructured":"Mart\u00edn-Guti\u00e9rrez D (2020) Twitter bots accounts. Kaggle.Com. 2020. https:\/\/doi.org\/10.34740\/KAGGLE\/DSV\/1623389","DOI":"10.34740\/KAGGLE\/DSV\/1623389"},{"key":"786_CR27","doi-asserted-by":"publisher","first-page":"54591","DOI":"10.1109\/ACCESS.2021.3068659","volume":"9","author":"D Martin-Gutierrez","year":"2021","unstructured":"D Martin-Gutierrez G Hernandez-Penaloza AB Hernandez A Lozano-Diez F Alvarez 2021 A deep learning approach for robust detection of bots in twitter using transformers IEEE Access 9 54591 54601 https:\/\/doi.org\/10.1109\/ACCESS.2021.3068659","journal-title":"IEEE Access"},{"key":"786_CR28","doi-asserted-by":"publisher","unstructured":"Minnich A, Nikan C, Danai K, Abdullah M (2017) \u201cBotWalk.\u201d In: Proceedings of the 2017 IEEE\/ACM international conference on advances in social networks analysis and mining 2017, New York, NY, USA: ACM, 467\u201374. https:\/\/doi.org\/10.1145\/3110025.3110163","DOI":"10.1145\/3110025.3110163"},{"key":"786_CR29","doi-asserted-by":"publisher","unstructured":"Morchid M, Richard D, Pierre-Michel B, Georges L, Juan-Manuel T-M (2014) Feature selection using principal component analysis for massive retweet detection. Pattern Recogn Lett 49:33\u201339. https:\/\/doi.org\/10.1016\/j.patrec.2014.05.020","DOI":"10.1016\/j.patrec.2014.05.020"},{"key":"786_CR30","doi-asserted-by":"publisher","unstructured":"Ostrowski DA (2014) Feature selection for Twitter classification. In: 2014 IEEE international conference on semantic computing, IEEE, 267\u201372. https:\/\/doi.org\/10.1109\/ICSC.2014.50","DOI":"10.1109\/ICSC.2014.50"},{"key":"786_CR31","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.ins.2017.08.063","volume":"421","author":"S Rathore","year":"2017","unstructured":"S Rathore PK Sharma V Loia Y-S Jeong JH Park 2017 Social network security: issues, challenges, threats, and solutions Inf Sci 421 43 69 https:\/\/doi.org\/10.1016\/j.ins.2017.08.063","journal-title":"Inf Sci"},{"key":"786_CR32","doi-asserted-by":"publisher","unstructured":"Shafahi M, Leon K, Hamideh A (2016) Phishing through social bots on Twitter. In: 2016 IEEE international conference on big data (big data), IEEE, 3703\u201312. https:\/\/doi.org\/10.1109\/BigData.2016.7841038","DOI":"10.1109\/BigData.2016.7841038"},{"key":"786_CR33","doi-asserted-by":"publisher","unstructured":"Shah FP, Vibha P (2016) A review on feature selection and feature extraction for text classification. In: 2016 international conference on wireless communications, signal processing and networking (WiSPNET), IEEE, 2264\u20132268. https:\/\/doi.org\/10.1109\/WiSPNET.2016.7566545","DOI":"10.1109\/WiSPNET.2016.7566545"},{"key":"786_CR34","doi-asserted-by":"publisher","unstructured":"Shukla H, Nakshatra J, Balaji P (2021) Enhanced Twitter bot detection using ensemble machine learning. In: 2021 6th international conference on inventive computation technologies (ICICT), IEEE, 930\u201336. https:\/\/doi.org\/10.1109\/ICICT50816.2021.9358734","DOI":"10.1109\/ICICT50816.2021.9358734"},{"key":"786_CR35","doi-asserted-by":"publisher","unstructured":"Stringhini G, Christopher K, Giovanni V (2010) Detecting spammers on social networks. In: Proceedings of the 26th annual computer security applications conference on\u2014ACSAC \u201910, New York, New York, USA: ACM Press, 1. https:\/\/doi.org\/10.1145\/1920261.1920263","DOI":"10.1145\/1920261.1920263"},{"issue":"6","key":"786_CR36","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MC.2016.183","volume":"49","author":"VS Subrahmanian","year":"2016","unstructured":"VS Subrahmanian A Azaria S Durst V Kagan A Galstyan K Lerman L Zhu E Ferrara A Flammini F Menczer 2016 The DARPA Twitter bot challenge Computer 49 6 38 46 https:\/\/doi.org\/10.1109\/MC.2016.183","journal-title":"Computer"},{"key":"786_CR37","doi-asserted-by":"publisher","unstructured":"Tang J, Salem A, Huan L (2014) Feature selection for classification: a review. In: Aggarwal CC (ed). Data classification: algorithms and applications, Chapman and Hall\/CRC. https:\/\/doi.org\/10.1201\/b17320","DOI":"10.1201\/b17320"},{"key":"786_CR38","unstructured":"Twitter.com (2020a) Automation rules 2020. https:\/\/help.twitter.com\/en\/rules-and-policies\/twitter-automation"},{"key":"786_CR39","unstructured":"Twitter.com (2020b) Data dictionary. https:\/\/developer.twitter.com\/en\/docs\/twitter-api\/v1\/data-dictionary\/overview\/user-object"},{"key":"786_CR40","unstructured":"Twitter.com (2020c) Twitter IDs. Twitter Inc 2020. https:\/\/developer.twitter.com\/en\/docs\/twitter-ids"},{"key":"786_CR41","doi-asserted-by":"crossref","unstructured":"Varol O, Emilio F, Davis CA, Filippo M, Alessandro F (2017) Human-bot interactions: detection, estimation, and characterization. CoRR abs\/1703.0. http:\/\/arxiv.org\/abs\/1703.03107","DOI":"10.1609\/icwsm.v11i1.14871"},{"key":"786_CR42","doi-asserted-by":"publisher","unstructured":"Visalakshi S, Radha V (2014) A literature review of feature selection techniques and applications: review of feature selection in data mining. In: 2014 IEEE international conference on computational intelligence and computing research,. IEEE, 1\u20136. https:\/\/doi.org\/10.1109\/ICCIC.2014.7238499","DOI":"10.1109\/ICCIC.2014.7238499"},{"key":"786_CR43","doi-asserted-by":"publisher","unstructured":"Wald R, Khoshgoftaar TM, Napolitano A (2013a) Should the same learners be used both within wrapper feature selection and for building classification models? In: 2013 IEEE 25th international conference on tools with artificial intelligence, IEEE, 439\u201345. https:\/\/doi.org\/10.1109\/ICTAI.2013.72","DOI":"10.1109\/ICTAI.2013.72"},{"key":"786_CR44","doi-asserted-by":"publisher","unstructured":"Wald R, Taghi K, Amri N (2013b) Filter- and wrapper-based feature selection for predicting user interaction with Twitter Bots. In: 2013 IEEE 14th international conference on information reuse and integration (IRI), IEEE, 416\u201323. https:\/\/doi.org\/10.1109\/IRI.2013.6642501","DOI":"10.1109\/IRI.2013.6642501"},{"key":"786_CR45","doi-asserted-by":"publisher","unstructured":"Wang AH (2010) Detecting spam bots in online social networking sites: a machine learning approach. In: DBSec, Springer, 10:335\u201342. https:\/\/doi.org\/10.1007\/978-3-642-13739-6_25","DOI":"10.1007\/978-3-642-13739-6_25"},{"issue":"01","key":"786_CR46","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1609\/aaai.v34i01.5460","volume":"34","author":"K-C Yang","year":"2020","unstructured":"K-C Yang O Varol P-M Hui F Menczer 2020 Scalable and generalizable social bot detection through data selection Proc AAAI Conf Artif Intell 34 01 1096 1103 https:\/\/doi.org\/10.1609\/aaai.v34i01.5460","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"786_CR47","doi-asserted-by":"publisher","unstructured":"Zhang, X, Shaoping Z, Wenxin L (2012) Detecting spam and promoting campaigns in the twitter social network. In: 2012 IEEE 12th international conference on data mining, IEEE, 1194\u201399. https:\/\/doi.org\/10.1109\/ICDM.2012.28","DOI":"10.1109\/ICDM.2012.28"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-021-00786-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-021-00786-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-021-00786-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T17:16:53Z","timestamp":1673284613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-021-00786-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,19]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["786"],"URL":"https:\/\/doi.org\/10.1007\/s13278-021-00786-4","relation":{},"ISSN":["1869-5450","1869-5469"],"issn-type":[{"value":"1869-5450","type":"print"},{"value":"1869-5469","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,19]]},"assertion":[{"value":"14 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We have no conflicts of interest or competing interests to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"84"}}