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Smart Cyber Victimization Discovery on Twitter

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dc.contributor.author Shoeibi, Niloufar es_ES
dc.contributor.author Shoeibi, Nastaran es_ES
dc.contributor.author Julian, Vicente es_ES
dc.contributor.author Ossowski, Sascha es_ES
dc.contributor.author González Arrieta, Angelica es_ES
dc.contributor.author Chamoso, Pablo es_ES
dc.date.accessioned 2023-01-09T07:38:36Z
dc.date.available 2023-01-09T07:38:36Z
dc.date.issued 2021-04-29 es_ES
dc.identifier.isbn 978-3-030-78900-8 es_ES
dc.identifier.issn 2367-3370 es_ES
dc.identifier.uri http://hdl.handle.net/10251/191075
dc.description.abstract [EN] The advancement of technologies, the promotion of smart-phones, and social networking have led to a high tendency among users to spend more time online interacting with each other via the available technologies. This is because they help overcome physical limitations and save time and energy by doing everything online. The rapid growth in this tendency has created the need for extra protection, by creating new rules and policies. However, sometimes users interrupt these rules and policies through unethical behavior. For example, bullying on social media platforms is a type of cyber victimization that can cause serious harm to individuals, leading to suicide. A firm step towards protecting the cyber society from victimization is to detect the topics that trigger the feeling of being a victim. In this paper, the focus is on Twitter, but it can be expanded to other platforms. The proposed method discovers cyber victimization by detecting the type of behavior leading to them being a victim. It consists of a text classification model, that is trained with a collected dataset of the official news since 2000, about suicide, self-harm, and cyberbullying. Results show that LinearSVC performs slightly better with an accuracy of 96%. es_ES
dc.description.sponsorship This research has been supported by the project "Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGE-Mobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security", Reference: RTI2018-095390-B-C31/32/33, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER). es_ES
dc.language Inglés es_ES
dc.publisher Springer es_ES
dc.relation.ispartof Sustainable Smart Cities and Territories. Lecture Notes in Networks and Systems (LNNS, volume 253) es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Twitter es_ES
dc.subject Cyberbullying es_ES
dc.subject Suicide and self-harm es_ES
dc.subject Cyber victim es_ES
dc.subject Text classification es_ES
dc.subject Text feature extraction es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Smart Cyber Victimization Discovery on Twitter es_ES
dc.type Comunicación en congreso es_ES
dc.type Artículo es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-78901-5_25 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095390-B-C31/ES/HACIA UNA MOVILIDAD INTELIGENTE Y SOSTENIBLE SOPORTADA POR SISTEMAS MULTI-AGENTES Y EDGE COMPUTING/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095390-B-C32/ES/MOVILIDAD INTELIGENTE Y SOSTENIBLE SOPORTADA POR SISTEMAS MULTI-AGENTES Y EDGE COMPUTING/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095390-B-C33/ES/MOVILIDAD INTELIGENTE Y SOSTENIBLE: INFRAESTRUCTURA Y TRANSPORTE COLABORATIVO/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Shoeibi, N.; Shoeibi, N.; Julian, V.; Ossowski, S.; González Arrieta, A.; Chamoso, P. (2021). Smart Cyber Victimization Discovery on Twitter. Springer. 289-299. https://doi.org/10.1007/978-3-030-78901-5_25 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename Sustainable Smart Cities and Territories International Conference (SSCt 2021) es_ES
dc.relation.conferencedate Abril 27-29,2021 es_ES
dc.relation.conferenceplace Doha, Qatar es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-78901-5_25 es_ES
dc.description.upvformatpinicio 289 es_ES
dc.description.upvformatpfin 299 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\459077 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.description.references Bussey, K., Luo, A., Fitzpatrick, S., Allison, K.: Defending victims of cyberbullying: the role of self-efficacy and moral disengagement. J. School Psychol. 78, 1–12 (2020) es_ES
dc.description.references Smith, P.K.: Research on cyberbullying: strengths and limitations. In: Vandebosch, H., Green, L. (eds.) Narratives in Research and Interventions on Cyberbullying Among Young People, pp. 9–27. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-04960-7_2 es_ES
dc.description.references Martínez-Monteagudo, M.C., Delgado, B., Díaz-Herrero, Á., García-Fernández, J.M.: Relationship between suicidal thinking, anxiety, depression and stress in university students who are victims of cyberbullying. Psychiatry Res. 286, 112856 (2020) es_ES
dc.description.references Raza, M.O., Memon, M., Bhatti, S., Bux, R.: Detecting cyberbullying in social commentary using supervised machine learning. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) FICC 2020. AISC, vol. 1130, pp. 621–630, Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39442-4_45 es_ES
dc.description.references Google news API es_ES
dc.description.references Shoeibi, N., Mateos, A.M., Camacho, A.R., Corchado, J.M.: A feature based approach on behavior analysis of the users on Twitter: a case study of AusOpen tennis championship. In: Dong, Y., Herrera-Viedma, E., Matsui, K., Omatsu, S., González Briones, A., Rodríguez González, S. (eds.) DCAI 2020. AISC, vol. 1237, pp. 284–294, Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53036-5_31 es_ES
dc.description.references Su, Y.-S., Wu, S.-Y.: Applying data mining techniques to explore user behaviors and watching video patterns in converged it environments. J. Ambient Intell. Humaniz. Comput. 1–8 (2021). https://doi.org/10.1007/s12652-020-02712-6 es_ES
dc.description.references Shoeibi, N.: Analysis of self-presentation and self-verification of the users on Twitter. In: Rodráguez González S., et al. (eds.) DCAI 2020. AISC, vol. 1242, pp. 221–226, Springer, Cham (2020). https://doi.org/10.1007/978-3-030-53829-3_25 es_ES
dc.description.references Marmo, R.: Social media mining. In: Encyclopedia of Organizational Knowledge, Administration, and Technology, pp. 2153–2165. IGI Global (2021) es_ES
dc.description.references Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Comput. Secur. 90, 101710 (2020) es_ES
dc.description.references Vyawahare, M., Chatterjee, M.: Taxonomy of cyberbullying detection and prediction techniques in online social networks. In: Jain, L.C., Tsihrintzis, G.A., Balas, V.E., Sharma, D.K. (eds.) Data Communication and Networks. AISC, vol. 1049, pp. 21–37. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0132-6_3 es_ES
dc.description.references Chamoso, P., González-Briones, A., De La Prieta, F., Venyagamoorthy, G.K., Corchado, J.M.: Smart city as a distributed platform: toward a system for citizen-oriented management. Comput. Commun. 152, 323–332 (2020) es_ES
dc.description.references Yigitcanlar, T., Butler, L., Windle, E., Desouza, K.C., Mehmood, R., Corchado, J.M.: Can building “artificially intelligent cities" safeguard humanity from natural disasters, pandemics, and other catastrophes? An urban scholar’s perspective. Sensors 20(10), 2988 (2020) es_ES
dc.description.references Casado-Vara, R., Rey, A.M.-d., Affes, S., Prieto, J., Corchado, J.M.: IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings. Future Gener. Comput. Syst. 102, 965–977 (2020) es_ES
dc.description.references González Bedia, M., Corchado Rodríguez, J.M., et al.: A planning strategy based on variational calculus for deliberative agents (2002) es_ES
dc.description.references Kumari, K., Singh, J.P., Dwivedi, Y.K., Rana, N.P.: Towards cyberbullying-free social media in smart cities: a unified multi-modal approach. Soft Comput. 24(15), 11059–11070 (2020) es_ES
dc.description.references Gencoglu, O.: Cyberbullying detection with fairness constraints. IEEE Internet Comput. 25, 20–29 (2020) es_ES
dc.description.references Muneer, A., Fati, S.M.: A comparative analysis of machine learning techniques for cyberbullying detection on Twitter. Future Internet 12(11), 187 (2020) es_ES
dc.description.references Cheng, L., Shu, K., Wu, S., Silva, Y.N., Hall, D.L., Liu, H.: Unsupervised cyberbullying detection via time-informed Gaussian mixture model. arXiv preprint arXiv:2008.02642 (2020) es_ES
dc.description.references Abbass, Z., Ali, Z., Ali, M., Akbar, B., Saleem, A.: A framework to predict social crime through Twitter tweets by using machine learning. In: 2020 IEEE 14th International Conference on Semantic Computing (ICSC), pp. 363–368 (2020) es_ES
dc.description.references Balakrishnan, V., Khan, S., Fernandez, T., Arabnia, H.R.: Cyberbullying detection on Twitter using big five and dark triad features. Pers. Individ. Differ. 141, 252–257 (2019) es_ES
dc.description.references Sadiq, S., Mehmood, A., Ullah, S., Ahmad, M., Choi, G.S., On, B.-W.: Aggression detection through deep neural model on twitter. Future Gener. Comput. Syst. 114, 120–129 (2021) es_ES
dc.description.references Corchado, J.M., Chamoso, P., Hernández, G., Gutierrez, A.S.R., Camacho, A.R., González-Briones, A., Pinto-Santos, F., Goyenechea, E., Garcia-Retuerta, D., Alonso-Miguel, M., et al.: Deepint. net: A rapid deployment platform for smart territories. Sensors 21(1), 236 (2021). Multidisciplinary Digital Publishing Institute es_ES


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