{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T16:21:34Z","timestamp":1781281294331,"version":"3.54.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s11042-022-12991-0","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T21:02:52Z","timestamp":1651698172000},"page":"39853-39871","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Spam SMS filtering based on text features and supervised machine learning techniques"],"prefix":"10.1007","volume":"81","author":[{"given":"Muhammad Adeel","family":"Abid","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saleem","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Abubakar","family":"Siddique","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Faheem","family":"Mushtaq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wajdi","family":"Aljedaani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"12991_CR1","doi-asserted-by":"crossref","unstructured":"Abid MA, Mushtaq MF, Akram U, Mughal B, Ahmad M, Imran M (2020) Recommending domain specific keywords for twitter. In: International conference on soft computing and data mining, Springer, pp 253\u2013263","DOI":"10.1007\/978-3-030-36056-6_25"},{"issue":"2","key":"12991_CR2","doi-asserted-by":"publisher","first-page":"183","DOI":"10.7763\/IJMLC.2014.V4.409","volume":"4","author":"I Ahmed","year":"2014","unstructured":"Ahmed I, Guan D, Chung T C (2014) Sms classification based on naive bayes classifier and apriori algorithm frequent itemset. Int J Mach Learn Comput 4(2):183","journal-title":"Int J Mach Learn Comput"},{"key":"12991_CR3","first-page":"95","volume":"106667","author":"B Alkhazi","year":"2020","unstructured":"Alkhazi B, DiStasi A, Aljedaani W, Alrubaye H, Ye X, Mkaouer M W (2020) Learning to rank developers for bug report assignment. Appl Soft Comput 106667:95","journal-title":"Appl Soft Comput"},{"key":"12991_CR4","doi-asserted-by":"crossref","unstructured":"AlOmar EA, Aljedaani W, Tamjeed M, Mkaouer MW, El-Glaly YN (2021) Finding the needle in a haystack: On the automatic identification of accessibility user reviews. In: Proceedings of the 2021 CHI conference on human factors in computing systems, pp 1\u201315","DOI":"10.1145\/3411764.3445281"},{"issue":"5","key":"12991_CR5","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1109\/TRO.2008.2004514","volume":"24","author":"A Angeli","year":"2008","unstructured":"Angeli A, Filliat D, Doncieux S, Meyer J A (2008) Fast and incremental method for loop-closure detection using bags of visual words. IEEE Trans Robot 24(5):1027\u20131037","journal-title":"IEEE Trans Robot"},{"key":"12991_CR6","unstructured":"Benevenuto F, Magno G, Rodrigues T, Almeida V (2010) Detecting spammers on twitter Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), vol 6, p 12"},{"key":"12991_CR7","volume-title":"(2017) Telephone Traffic forecasting of electric system based on multi-factor decomposition. In: 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science","author":"H Bo","year":"2017","unstructured":"Bo H, Xiao-Ling R, ZHANG C J, Qin H Q, Chong-Hui G (2017) (2017) Telephone Traffic forecasting of electric system based on multi-factor decomposition. In: 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science. Atlantis Press, EEEIS"},{"key":"12991_CR8","first-page":"2","volume":"50","author":"A Cernian","year":"2016","unstructured":"Cernian A, Carstoiu D, Olteanu A, Sgarciu V (2016) Assessing the performance of compression based clustering for text mining. Econ Comput Econ Cybern Stud Res 50:2","journal-title":"Econ Comput Econ Cybern Stud Res"},{"key":"12991_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"12991_CR10","doi-asserted-by":"crossref","unstructured":"Cormack GV, Hidalgo JMG, S\u00e1nz EP (2007) Feature engineering for mobile (sms) spam filtering. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, pp 871\u2013872","DOI":"10.1145\/1277741.1277951"},{"key":"12991_CR11","unstructured":"Dittman DJ, Khoshgoftaar TM, Wald R, Napolitano A (2014) Comparison of data sampling approaches for imbalanced bioinformatics data. In: The twenty-seventh international FLAIRS conference"},{"key":"12991_CR12","unstructured":"Doma V, Kendre S, Bhagwat L (2018) Detecting hate speech and offensive language on twitter using machine learning: An n-gram and tfidf based approach. arXiv:180908651"},{"key":"12991_CR13","unstructured":"Duc G M, Manh L, et al. (2016) A novel method to improve the speed and the accuracy of location prediction algorithm of mobile users for cellular networks. Chuy\u00ea,n san C\u00e1c c\u00f4ng tr\u00ecnh nghi\u00ean cu, ph\u00e1t trin v\u00e0 ng dng C\u00f4ng ngh th\u00f4ng tin v\u00e0 Truyn th\u00f4ng"},{"issue":"2","key":"12991_CR14","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1109\/TBC.2019.2912619","volume":"65","author":"M Fallgren","year":"2019","unstructured":"Fallgren M, Abbas T, Allio S, Alonso-Zarate J, Fodor G, Gallo L, Kousaridas A, Li Y, Li Z, Li Z et al (2019) Multicast and broadcast enablers for high-performing cellular v2x systems. IEEE Trans Broadcast 65(2):454\u2013463","journal-title":"IEEE Trans Broadcast"},{"issue":"11","key":"12991_CR15","doi-asserted-by":"publisher","first-page":"7307","DOI":"10.1007\/s00500-021-05689-2","volume":"25","author":"F Fang","year":"2021","unstructured":"Fang F, Wu J, Li Y, Ye X, Aljedaani W, Mkaouer MW (2021) On the classification of bug reports to improve bug localization. Soft Comput 25(11):7307\u20137323","journal-title":"Soft Comput"},{"key":"12991_CR16","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.inffus.2018.08.002","volume":"48","author":"H Faris","year":"2019","unstructured":"Faris H, Ala\u2019m AZ, Heidari AA, Aljarah I, Mafarja M, Hassonah MA, Fujita H (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Information Fusion 48:67\u201383","journal-title":"Information Fusion"},{"key":"12991_CR17","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garcia S, Herrera F, Chawla N V (2018) Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J Artif Intell Res 61:863\u2013905","journal-title":"J Artif Intell Res"},{"issue":"1","key":"12991_CR18","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3390\/f10010025","volume":"10","author":"JS Fraser","year":"2019","unstructured":"Fraser J S, Wang W J, He H S, Thompson F R (2019) Modeling post-fire tree mortality using a logistic regression method within a forest landscape model. Forests 10(1):25","journal-title":"Forests"},{"key":"12991_CR19","doi-asserted-by":"crossref","unstructured":"Gadde S, Lakshmanarao A, Satyanarayana S (2021) Sms spam detection using machine learning and deep learning techniques 2021 7Th international conference on advanced computing and communication systems (ICACCS), vol 1, pp 358\u2013362, DOI 10.1109\/ICACCS51430.2021.9441783","DOI":"10.1109\/ICACCS51430.2021.9441783"},{"issue":"6","key":"12991_CR20","first-page":"16","volume":"148","author":"B Gayathri","year":"2016","unstructured":"Gayathri B, Sumathi C (2016) An automated technique using gaussian na\u00efve bayes classifier to classify breast cancer. Int J Comput Appl 148(6):16\u201321","journal-title":"Int J Comput Appl"},{"key":"12991_CR21","doi-asserted-by":"crossref","unstructured":"Ghosh A, Maeder A, Baker M, Chandramouli D (2019). 5g evolution: A view on 5g cellular technology beyond 3gpp release 15. IEEE Access 7:127639\u2013127651","DOI":"10.1109\/ACCESS.2019.2939938"},{"key":"12991_CR22","doi-asserted-by":"crossref","unstructured":"G\u00f3mez Hidalgo JM, Bringas GC, S\u00e1nz EP, Garc\u00eda FC (2006) Content based sms spam filtering. In: Proceedings of the 2006 ACM symposium on Document engineering, pp 107\u2013114","DOI":"10.1145\/1166160.1166191"},{"key":"12991_CR23","doi-asserted-by":"crossref","unstructured":"Ishtiaq A, Islam M A, Iqbal M A, Aleem M, Ahmed U (2019) Graph centrality based spam sms detection. In: 2019 16Th international bhurban conference on applied sciences and technology. IEEE, IBCAST, pp 629\u2013633","DOI":"10.1109\/IBCAST.2019.8667174"},{"key":"12991_CR24","first-page":"7","volume":"e645","author":"R Jamil","year":"2021","unstructured":"Jamil R, Ashraf I, Rustam F, Saad E, Mehmood A, Choi G S (2021) Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model. PeerJ Computer Science e645:7","journal-title":"PeerJ Computer Science"},{"key":"12991_CR25","unstructured":"Kaggle (2016) Sms spam collection dataset. https:\/\/www.kaggle.com\/uciml\/sms-spam-collection-dataset\/. Accessed 20 Apr 2021"},{"key":"12991_CR26","unstructured":"Kaggle (2021) Spam mails dataset. https:\/\/www.kaggle.com\/venky73\/spam-mails-dataset. Accessed 24 Apr 2021"},{"key":"12991_CR27","first-page":"3146","volume":"30","author":"G Ke","year":"2017","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T Y (2017) Lightgbm: a highly efficient gradient boosting decision tree. Advances in neural information processing systems 30:3146\u20133154","journal-title":"Advances in neural information processing systems"},{"key":"12991_CR28","doi-asserted-by":"crossref","unstructured":"Lee H Y, Kang SS (2019) Word embedding method of sms messages for spam message filtering, IEEE, BigComp","DOI":"10.1109\/BIGCOMP.2019.8679476"},{"key":"12991_CR29","first-page":"18","volume":"4","author":"MC Lee","year":"2012","unstructured":"Lee MC, Chang JW, Hsieh TC, Chen HH, Chen CH (2012) A sentence similarity metric based on semantic patterns. Adv Inf Sci Serv Sci 4:18","journal-title":"Adv Inf Sci Serv Sci"},{"key":"12991_CR30","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409","author":"WC Lin","year":"2017","unstructured":"Lin W C, Tsai C F, Hu Y H, Jhang J S (2017) Clustering-based undersampling in class-imbalanced data. Inf Sci 409:17\u201326","journal-title":"Inf Sci"},{"issue":"18","key":"12991_CR31","doi-asserted-by":"publisher","first-page":"8438","DOI":"10.3390\/app11188438","volume":"11","author":"M Mujahid","year":"2021","unstructured":"Mujahid M, Lee E, Rustam F, Washington P B, Ullah S, Reshi A A, Ashraf I (2021) Sentiment analysis and topic modeling on tweets about online education during covid-19. Appl Sci 11(18):8438","journal-title":"Appl Sci"},{"issue":"1","key":"12991_CR32","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1177\/0165551515616310","volume":"43","author":"NK Nagwani","year":"2017","unstructured":"Nagwani N K, Sharaff A (2017) Sms spam filtering and thread identification using bi-level text classification and clustering techniques. J Inf Sci 43 (1):75\u201387","journal-title":"J Inf Sci"},{"key":"12991_CR33","unstructured":"Nikam S, Chaudhari R (2017) A review paper on image spam filtering"},{"issue":"4","key":"12991_CR34","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1109\/4233.737581","volume":"2","author":"S Pavlopoulos","year":"1998","unstructured":"Pavlopoulos S, Kyriacou E, Berler A, Dembeyiotis S, Koutsouris D (1998) A novel emergency telemedicine system based on wireless communication technology-ambulance. IEEE Trans Inf Technol Biomed 2(4):261\u2013267","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"8","key":"12991_CR35","first-page":"1018","volume":"33","author":"J Ramsingh","year":"2021","unstructured":"Ramsingh J, Bhuvaneswari V (2021) An efficient map reduce-based hybrid nbc-tfidf algorithm to mine the public sentiment on diabetes mellitus\u2013a big data approach. J King Saud University Comput Inf Sci 33(8):1018\u20131029","journal-title":"J King Saud University Comput Inf Sci"},{"key":"12991_CR36","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1016\/j.future.2019.09.001","volume":"102","author":"PK Roy","year":"2020","unstructured":"Roy P K, Singh J P, Banerjee S (2020) Deep learning to filter sms spam. Futur Gener Comput Syst 102:524\u2013533","journal-title":"Futur Gener Comput Syst"},{"key":"12991_CR37","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.7717\/peerj-cs.745","volume":"7","author":"V Rupapara","year":"2021","unstructured":"Rupapara V, Rustam F, Amaar A, Washington PB, Lee E, Ashraf I (2021a) Deepfake tweets classification using stacked bi-lstm and words embedding. PeerJ Computer Science 7:e745","journal-title":"PeerJ Computer Science"},{"key":"12991_CR38","doi-asserted-by":"crossref","unstructured":"Rupapara V, Rustam F, Shahzad HF, Mehmood A, Ashraf I, Choi GS (2021b) Impact of smote on imbalanced text features for toxic comments classification using rvvc model. IEEE Access","DOI":"10.1109\/ACCESS.2021.3083638"},{"issue":"10","key":"12991_CR39","doi-asserted-by":"publisher","first-page":"4361","DOI":"10.1021\/acs.molpharmaceut.8b00546","volume":"15","author":"DP Russo","year":"2018","unstructured":"Russo D P, Zorn K M, Clark A M, Zhu H, Ekins S (2018) Comparing multiple machine learning algorithms and metrics for estrogen receptor binding prediction. Mol Pharm 15(10):4361\u20134370","journal-title":"Mol Pharm"},{"issue":"11","key":"12991_CR40","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.3390\/e21111078","volume":"21","author":"F Rustam","year":"2019","unstructured":"Rustam F, Ashraf I, Mehmood A, Ullah S, Choi G S (2019) Tweets classification on the base of sentiments for us airline companies. Entropy 21(11):1078","journal-title":"Entropy"},{"key":"12991_CR41","doi-asserted-by":"crossref","unstructured":"Safdari N, Alrubaye H, Aljedaani W, Baez BB, DiStasi A, Mkaouer MW (2019) Learning to rank faulty source files for dependent bug reports. In: Big Data: learning, analytics, and applications, international society for optics and photonics, vol 10989, p 109890B","DOI":"10.1117\/12.2519226"},{"issue":"1","key":"12991_CR42","first-page":"1","volume":"1","author":"H Sajedi","year":"2016","unstructured":"Sajedi H, Parast G Z, Akbari F (2016) Sms spam filtering using machine learning techniques: a survey. Mach Learn Res 1(1):1","journal-title":"Mach Learn Res"},{"key":"12991_CR43","doi-asserted-by":"publisher","first-page":"15650","DOI":"10.1109\/ACCESS.2017.2666785","volume":"5","author":"MA Shafi\u2019I","year":"2017","unstructured":"Shafi\u2019I MA, Abd Latiff MS, Chiroma H, Osho O, Abdul-Salaam G, Abubakar AI, Herawan T (2017) A review on mobile sms spam filtering techniques. IEEE Access 5:15650\u201315666","journal-title":"IEEE Access"},{"key":"12991_CR44","doi-asserted-by":"crossref","unstructured":"Sisodia DS, Mahapatra S, Sharma A (2020) Automated sms classification and spam analysis using topic modeling. In: 2nd International Conference on Data, Engineering and Applications (IDEA), pp 1\u20136","DOI":"10.1109\/IDEA49133.2020.9170710"},{"key":"12991_CR45","doi-asserted-by":"crossref","unstructured":"Sohn DN, Lee JT, Rim HC (2009) The contribution of stylistic information to content-based mobile spam filtering. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp 321\u2013324","DOI":"10.3115\/1667583.1667682"},{"key":"12991_CR46","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.chemolab.2019.01.002","volume":"185","author":"JL Speiser","year":"2019","unstructured":"Speiser JL, Wolf BJ, Chung D, Karvellas CJ, Koch DG, Durkalski VL (2019) Bimm forest: a random forest method for modeling clustered and longitudinal binary outcomes. Chemometr Intell Lab Syst 185:122\u2013134","journal-title":"Chemometr Intell Lab Syst"},{"issue":"12","key":"12991_CR47","first-page":"1869","volume":"5","author":"T Subramaniam","year":"2010","unstructured":"Subramaniam T, Jalab HA, Taqa AY (2010) Overview of textual anti-spam filtering techniques. Int J Phys Sci 5(12):1869\u20131882","journal-title":"Int J Phys Sci"},{"key":"12991_CR48","unstructured":"VRL N (2009) An unsupervised approach to domain-specific term extraction. In: Australasian language technology association workshop, vol 2009, p 94"},{"issue":"6","key":"12991_CR49","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1109\/JPROC.2005.849717","volume":"93","author":"A Willig","year":"2005","unstructured":"Willig A, Matheus K, Wolisz A (2005) Wireless technology in industrial networks. Proc IEEE 93(6):1130\u20131151","journal-title":"Proc IEEE"},{"issue":"14","key":"12991_CR50","doi-asserted-by":"publisher","first-page":"5011","DOI":"10.3390\/app10145011","volume":"10","author":"T Xia","year":"2020","unstructured":"Xia T, Chen X (2020) A discrete hidden markov model for sms spam detection. Appl Sci 10(14):5011","journal-title":"Appl Sci"},{"issue":"15","key":"12991_CR51","first-page":"325","volume":"119","author":"YK Zamel","year":"2018","unstructured":"Zamel Y K, Ali S A, Naser M A (2018) Analysis study of spam image-based emails filtering techniques. Int J Pur Appl Math 119(15):325\u2013346","journal-title":"Int J Pur Appl Math"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12991-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12991-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12991-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T09:32:51Z","timestamp":1666690371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12991-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":51,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["12991"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12991-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,4]]},"assertion":[{"value":"20 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}