{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:27:59Z","timestamp":1760711279706,"version":"3.37.3"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e0 degli Studi di Bari Aldo Moro"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This study establishes a correlation between computer science and psychology, specifically focusing on the incorporation of smartphone sensors and users' personality index. A limited number of state-of-the-art approaches have considered these factors, while no existing dataset currently encompasses this correlation. In this study, an Android application was developed to implement a questionnaire on bullying and cyberbullying, using smartphone sensors to predict Personal Index. Sensor data are collected in the \u201c<jats:italic>UNIBA HAR Dataset<\/jats:italic>\u201d and were analyzed using AI algorithms to find a correlation between the categorization class of the questionnaire (<jats:italic>Personality Index<\/jats:italic>) and the prediction of ML behavioral models. The results indicate that the Bayesian Bridge with <jats:italic>\"Bullying bully vs. Victimization bullying\"<\/jats:italic> and <jats:italic>\"Total bullying vs. Total victimization\"<\/jats:italic> performs better on average 0.94 accuracy, and the LSTM with the last categorization performs 0.89 accuracy. These results are crucial for future development in the same research area.<\/jats:p>\n                <jats:p><jats:bold>Graphical abstract<\/jats:bold><\/jats:p>","DOI":"10.1007\/s11042-023-17609-7","type":"journal-article","created":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T06:01:30Z","timestamp":1699682490000},"page":"51291-51320","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Classification bullying\/cyberbullying through smartphone sensor and a questionnaire application"],"prefix":"10.1007","volume":"83","author":[{"given":"Vito Nicola","family":"Convertini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9974-9414","authenticated-orcid":false,"given":"Vincenzo","family":"Gattulli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donato","family":"Impedovo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Grazia","family":"Terrone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"key":"17609_CR1","doi-asserted-by":"publisher","unstructured":"Reynolds K, Kontostathis A, Edwards L (2011) Using machine learning to detect cyberbullying. Proceedings - 10th International Conference on Machine Learning and Applications. ICMLA 2:241\u2013244. https:\/\/doi.org\/10.1109\/ICMLA.2011.152","DOI":"10.1109\/ICMLA.2011.152"},{"key":"17609_CR2","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/J.CHB.2012.05.024","volume":"29","author":"R Slonje","year":"2013","unstructured":"Slonje R, Smith PK, Fris\u00e9n A (2013) The nature of cyberbullying, and strategies for prevention. Comput Human Behav 29:26\u201332. https:\/\/doi.org\/10.1016\/J.CHB.2012.05.024","journal-title":"Comput Human Behav"},{"key":"17609_CR3","unstructured":"Impedovo D, Longo A, Palmisano T, Sarcinella L, Veneto D (2022) An investigation on voice mimicry attacks to a speaker recognition system. Italian Conference on Cybersecurity. https:\/\/api.semanticscholar.org\/CorpusID:253270215"},{"key":"17609_CR4","unstructured":"Indagine conoscitiva su bullismo e cyberbullismo. https:\/\/www.istat.it\/it\/archivio\/228976. Accessed 1 Mar 2023"},{"key":"17609_CR5","doi-asserted-by":"publisher","unstructured":"Bangpeng Y, Jiang X, Khosla A, Lin A, Guibas L, Li F (2011) Human action recognition by learning bases of action attributes and parts.  https:\/\/doi.org\/10.1109\/ICCV.2011.6126386","DOI":"10.1109\/ICCV.2011.6126386"},{"key":"17609_CR6","doi-asserted-by":"publisher","unstructured":"Uddin MZ, Soylu A (2021) Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning. Sci Rep. 2021 Aug 12. https:\/\/doi.org\/10.1038\/s41598-021-95947-y","DOI":"10.1038\/s41598-021-95947-y"},{"key":"17609_CR7","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1080\/13811118.2010.494133","volume":"14","author":"S Hinduja","year":"2010","unstructured":"Hinduja S, Patchin JW (2010) Bullying, cyberbullying, and suicide. Arch Suicide Res 14:206\u2013221. https:\/\/doi.org\/10.1080\/13811118.2010.494133","journal-title":"Arch Suicide Res"},{"key":"17609_CR8","first-page":"114","volume":"3260","author":"D Impedovo","year":"2022","unstructured":"Impedovo D, Longo A, Palmisano T et al (2022) An investigation on voice mimicry attacks to a speaker recognition system. CEUR Workshop Proc 3260:114\u2013123","journal-title":"CEUR Workshop Proc"},{"key":"17609_CR9","doi-asserted-by":"publisher","unstructured":"Sharaff A, Nagwani NK, Dhadse A (2016)  Comparative study of classification algorithms for spam email detection. Emerging Research in Computing, Information. Communication and Applications. Springer. https:\/\/doi.org\/10.1007\/978-81-322-2553-9_23","DOI":"10.1007\/978-81-322-2553-9_23"},{"key":"17609_CR10","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1177\/0165551515616310\/ASSET\/IMAGES\/LARGE","volume":"43","author":"NK Nagwani","year":"2017","unstructured":"Nagwani NK, Sharaff A (2017) SMS spam filtering and thread identification using bi-level text classification and clustering techniques. J Inf Sci 43:75\u201387. https:\/\/doi.org\/10.1177\/0165551515616310\/ASSET\/IMAGES\/LARGE","journal-title":"J Inf Sci"},{"key":"17609_CR11","doi-asserted-by":"publisher","first-page":"55","DOI":"10.33899\/CSMJ.2021.168253","volume":"15","author":"A Kh","year":"2021","unstructured":"Kh A, Ibrahim L (2021) Survey on Human Activity Recognition using Smartphone. AL-Rafidain J Comput Sci Mathem 15:55\u201367. https:\/\/doi.org\/10.33899\/CSMJ.2021.168253","journal-title":"AL-Rafidain J Comput Sci Mathem"},{"key":"17609_CR12","doi-asserted-by":"publisher","first-page":"107561","DOI":"10.1016\/J.PATCOG.2020.107561","volume":"108","author":"L Minh Dang","year":"2020","unstructured":"Minh Dang L, Min K, Wang H et al (2020) Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognit 108:107561. https:\/\/doi.org\/10.1016\/J.PATCOG.2020.107561","journal-title":"Pattern Recognit"},{"key":"17609_CR13","doi-asserted-by":"publisher","first-page":"261","DOI":"10.3390\/ELECTRONICS12020261","volume":"12","author":"V Gattulli","year":"2023","unstructured":"Gattulli V, Impedovo D, Pirlo G, Sarcinella L (2023) Human Activity Recognition for the Identification of Bullying and Cyberbullying Using Smartphone Sensors. Electronics 12:261. https:\/\/doi.org\/10.3390\/ELECTRONICS12020261","journal-title":"Electronics"},{"key":"17609_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3209084","author":"F Luo","year":"2022","unstructured":"Luo F, Khan S, Huang Y, Wu K (2022) Activity-based person identification using multimodal wearable sensor data. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2022.3209084","journal-title":"IEEE Internet Things J"},{"key":"17609_CR15","doi-asserted-by":"publisher","unstructured":"Straczkiewicz M, Huang EJ, Onnela JP (2023) A \u201cone-size-fits-most\u201d walking recognition method for smartphones, smartwatches, and wearable accelerometers. NPJ Digit Med 6:1 6:1\u201316. https:\/\/doi.org\/10.1038\/s41746-022-00745-z","DOI":"10.1038\/s41746-022-00745-z"},{"key":"17609_CR16","doi-asserted-by":"publisher","unstructured":"Wang Q, Fu M, Wang J, Sun L, Huang R, Li X, Jiang Z (2023) A smartphone-based zero-effort method for mitigating epidemic propagation. EURASIP J Adv Signal Process. https:\/\/doi.org\/10.1186\/s13634-023-00984-6","DOI":"10.1186\/s13634-023-00984-6"},{"key":"17609_CR17","doi-asserted-by":"publisher","unstructured":"Hu M, Zhang K, You R, Tu B (2023) AuthConFormer: Sensor-based continuous authentication of smartphone users using a convolutional transformer. Comput Sec. https:\/\/doi.org\/10.1016\/j.cose.2023.103122","DOI":"10.1016\/j.cose.2023.103122"},{"key":"17609_CR18","doi-asserted-by":"publisher","unstructured":"Rayani PK, Changder S (2023) Sensor-based continuous user authentication on smartphone through machine learning. Microprocess Microsyst 104750. https:\/\/doi.org\/10.1016\/J.MICPRO.2022.104750","DOI":"10.1016\/J.MICPRO.2022.104750"},{"key":"17609_CR19","doi-asserted-by":"publisher","unstructured":"Alzahrani S, Alderaan J, Alatawi D, Alotaibi B (2023) Continuous mobile user authentication using a hybrid CNN-Bi-LSTM approach.  Comput Mater Continua 651\u2013667. https:\/\/doi.org\/10.32604\/CMC.2023.035173","DOI":"10.32604\/CMC.2023.035173"},{"key":"17609_CR20","doi-asserted-by":"publisher","first-page":"35909","DOI":"10.1007\/S11042-020-09146-4","volume":"79","author":"F Balducci","year":"2020","unstructured":"Balducci F, Impedovo D, Macchiarulo N, Pirlo G (2020) Affective states recognition through touch dynamics. Multimed Tools Appl 79:35909\u201335926. https:\/\/doi.org\/10.1007\/S11042-020-09146-4","journal-title":"Multimed Tools Appl"},{"key":"17609_CR21","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.cose.2016.03.003","volume":"59","author":"PS Teh","year":"2016","unstructured":"Teh PS, Zhang N, Teoh ABJ, Chen K (2016) A Survey on Touch Dynamics Authentication in Mobile Devices. Comput Secur 59:210\u2013235","journal-title":"Comput Secur"},{"key":"17609_CR22","doi-asserted-by":"publisher","unstructured":"Nerini M, Favarelli E, Chiani M (2022) Augmented PIN authentication through behavioral biometrics. Sensors. https:\/\/doi.org\/10.3390\/s22134857","DOI":"10.3390\/s22134857"},{"key":"17609_CR23","doi-asserted-by":"publisher","first-page":"4019","DOI":"10.1007\/S12652-019-01654-Y\/FIGURES\/12","volume":"11","author":"PS Teh","year":"2020","unstructured":"Teh PS, Zhang N, Tan SY et al (2020) Strengthen user authentication on mobile devices by using user\u2019s touch dynamics pattern. J Ambient Intell Humaniz Comput 11:4019\u20134039. https:\/\/doi.org\/10.1007\/S12652-019-01654-Y\/FIGURES\/12","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"17609_CR24","doi-asserted-by":"publisher","first-page":"15803","DOI":"10.1007\/S11042-020-10446-Y","volume":"80","author":"R Zaccagnino","year":"2021","unstructured":"Zaccagnino R, Capo C, Guarino A et al (2021) Techno-regulation and intelligent safeguards: Analysis of touch gestures for online child protection. Multimed Tools Appl 80:15803\u201315824. https:\/\/doi.org\/10.1007\/S11042-020-10446-Y","journal-title":"Multimed Tools Appl"},{"key":"17609_CR25","doi-asserted-by":"publisher","unstructured":"Ozkul D (2022) Children\u2019s mobile communicative practices and locational privacy. J Comput-Mediated Comm 27. https:\/\/doi.org\/10.1093\/jcmc\/zmac015","DOI":"10.1093\/jcmc\/zmac015"},{"key":"17609_CR26","doi-asserted-by":"crossref","unstructured":"Gattulli V, Impedovo D, Sarcinella L (2023) Anomaly detection using smartphone sensors for a bullying detection. WorldCIST'23-11st World Conference on Information Systems and Technologies","DOI":"10.1007\/978-3-031-45651-0_33"},{"key":"17609_CR27","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1002\/AB.21636","volume":"42","author":"BE Palladino","year":"2016","unstructured":"Palladino BE, Nocentini A, Menesini E (2016) Evidence-based intervention against bullying and cyberbullying: Evaluation of the NoTrap! program in two independent trials. Aggress Behav 42:194\u2013206. https:\/\/doi.org\/10.1002\/AB.21636","journal-title":"Aggress Behav"},{"key":"17609_CR28","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1089\/CYBER.2014.0366","volume":"18","author":"BE Palladino","year":"2015","unstructured":"Palladino BE, Nocentini A, Menesini E (2015) Psychometric properties of the Florence CyberBullying-CyberVictimization Scales. Cyberpsychol Behav Soc Netw 18:112\u2013119. https:\/\/doi.org\/10.1089\/CYBER.2014.0366","journal-title":"Cyberpsychol Behav Soc Netw"},{"key":"17609_CR29","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1089\/CYBER.2018.0731","volume":"22","author":"O Lopez-Fernandez","year":"2019","unstructured":"Lopez-Fernandez O, Griffiths MD, Kuss DJ et al (2019) Cross-Cultural Validation of the Compulsive Internet Use Scale in Four Forms and Eight Languages. Cyberpsychol Behav Soc Netw 22:451\u2013464. https:\/\/doi.org\/10.1089\/CYBER.2018.0731","journal-title":"Cyberpsychol Behav Soc Netw"},{"key":"17609_CR30","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/TASSP.1980.1163351","volume":"28","author":"J Makhoul","year":"1980","unstructured":"Makhoul J (1980) A Fast Cosine Transform in One and Two Dimensions. IEEE Trans Acoust 28:27\u201334. https:\/\/doi.org\/10.1109\/TASSP.1980.1163351","journal-title":"IEEE Trans Acoust"},{"key":"17609_CR31","unstructured":"Ptaszynski M, Dybala P, Matsuba T, Fumito M, Rafal R, Kenji A (2010) Machine learning and affect analysis against cyber-bullying. Linguistic And Cognitive Approaches To Dialog Agents Symposium"},{"key":"17609_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/A16010001","volume":"16","author":"M Chimienti","year":"2022","unstructured":"Chimienti M, Danzi I, Impedovo D et al (2022) MIRROR: Methodological Innovation to Remodel the Electric Loads to Reduce Economic OR Environmental Impact of User. Algorithms 16:1. https:\/\/doi.org\/10.3390\/A16010001","journal-title":"Algorithms"},{"key":"17609_CR33","doi-asserted-by":"publisher","unstructured":"Impedovo D, Dentamaro V, Pirlo G, Sarcinella L (2019) TrafficWave: Generative deep learning architecture for vehicular traffic flow prediction. Appl Sci 9(24):5504. https:\/\/doi.org\/10.3390\/app9245504","DOI":"10.3390\/app9245504"},{"key":"17609_CR34","doi-asserted-by":"publisher","first-page":"6099","DOI":"10.3390\/APP13106099","volume":"13","author":"F Castro","year":"2023","unstructured":"Castro F, Impedovo D, Pirlo G (2023) A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission. Appl Sci 13:6099. https:\/\/doi.org\/10.3390\/APP13106099","journal-title":"Appl Sci"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17609-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17609-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17609-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T07:37:09Z","timestamp":1715758629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17609-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,11]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17609"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17609-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,11,11]]},"assertion":[{"value":"11 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2023","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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}