{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:45:53Z","timestamp":1743147953742,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031044465"},{"type":"electronic","value":"9783031044472"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-04447-2_15","type":"book-chapter","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T16:05:24Z","timestamp":1650557124000},"page":"223-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classical Machine Learning vs Deep Learning for Detecting Cyber-Violence in Social Media"],"prefix":"10.1007","author":[{"given":"Randa","family":"Zarnoufi","sequence":"first","affiliation":[]},{"given":"Mounia","family":"Abik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,20]]},"reference":[{"key":"15_CR1","unstructured":"Sanchez, H., Kumar, S.: Twitter bullying detection. In: NSDI, pp. 15\u201322 (2011)"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Kowalski, R.M., Giumetti, G.W., Schroeder, A.N., Lattanner, M.R.: Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol. Bull. \u00a9 2014 Am. Psychol. Assoc. 140, 1073\u20131137 (2014)","DOI":"10.1037\/a0035618"},{"key":"15_CR3","first-page":"640","volume":"24","author":"S Paul","year":"2012","unstructured":"Paul, S., Smith, P.K., Blumberg, H.H.: Investigating legal aspects of cyberbullying. Psicothema 24, 640\u2013645 (2012)","journal-title":"Psicothema"},{"key":"15_CR4","unstructured":"Davahli, M.R., et al.: Personality and text: quantitative psycholinguistic analysis of a stylistically differentiated Czech text. Psychol. Stud. (Mysore). 12, 1\u201323 (2020)"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Moreno, J.D., Mart\u00ednez-Huertas, J., Olmos, R., Jorge-Botana, G., Botella, J.: Can personality traits be measured analyzing written language? A meta-analytic study on computational methods. Pers. Individ. Dif. 177 (2021)","DOI":"10.1016\/j.paid.2021.110818"},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/0261927X09351676","volume":"29","author":"YR Tausczik","year":"2010","unstructured":"Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29, 24\u201354 (2010)","journal-title":"J. Lang. Soc. Psychol."},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.jrp.2010.04.001","volume":"44","author":"T Yarkoni","year":"2010","unstructured":"Yarkoni, T.: Personality in 100,000 words: a large-scale analysis of personality and word use among bloggers. J. Res. Pers. 44, 363\u2013373 (2010)","journal-title":"J. Res. Pers."},{"key":"15_CR8","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.paid.2017.12.018","volume":"124","author":"D Azucar","year":"2018","unstructured":"Azucar, D., Marengo, D., Settanni, M.: Predicting the big 5 personality traits from digital footprints on social media: a meta-analysis. Pers. Individ. Dif. 124, 150\u2013159 (2018)","journal-title":"Pers. Individ. Dif."},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Schwartz, H.A., et al.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8, e73791 (2013)","DOI":"10.1371\/journal.pone.0073791"},{"key":"15_CR10","first-page":"1","volume":"3045","author":"S Salawu","year":"2017","unstructured":"Salawu, S., He, Y., Lumsden, J.: Approaches to automated detection of cyberbullying: a survey. IEEE Trans. Affect. Comput. 3045, 1\u201320 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.paid.2019.01.024","volume":"141","author":"V Balakrishnan","year":"2019","unstructured":"Balakrishnan, V., Khan, S., Fernandez, T., Arabnia, H.R.: Cyberbullying detection on twitter using big five and dark triad features. Pers. Individ. Dif. 141, 252\u2013257 (2019)","journal-title":"Pers. Individ. Dif."},{"key":"15_CR12","series-title":"Learning and Analytics in Intelligent Systems","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/978-3-030-36778-7_21","volume-title":"Innovation in Information Systems and Technologies to Support Learning Research","author":"R Zarnoufi","year":"2020","unstructured":"Zarnoufi, R., Abik, M.: Big five personality traits and ensemble machine learning to detect cyber-violence in social media. In: Serrhini, M., Silva, C., Aljahdali, S. (eds.) EMENA-ISTL 2019. LAIS, vol. 7, pp. 194\u2013202. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-36778-7_21"},{"key":"15_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-3-642-31178-9_34","volume-title":"Natural Language Processing and Information Systems","author":"M Dadvar","year":"2012","unstructured":"Dadvar, M., Ordelman, R., de Jong, F., Trieschnigg, D.: Towards user modelling in the combat against cyberbullying. In: Bouma, G., Ittoo, A., M\u00e9tais, E., Wortmann, H. (eds.) NLDB 2012. LNCS, vol. 7337, pp. 277\u2013283. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-31178-9_34"},{"key":"15_CR14","first-page":"182","volume":"9","author":"R Zarnoufi","year":"2020","unstructured":"Zarnoufi, R., Boutbi, M., Abik, M.: AI to prevent cyber-violence: harmful behaviour detection in social media. Int. J. High Perform. Syst. Arch. 9, 182\u2013191 (2020)","journal-title":"Int. J. High Perform. Syst. Arch."},{"key":"15_CR15","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.chb.2016.05.051","volume":"63","author":"MA Algaradi","year":"2016","unstructured":"Algaradi, M.A., Varathan, K.D., Ravana, S.D.: Computers in human behavior cybercrime detection in online communications: the experimental case of cyberbullying detection in the Twitter network. Comput. Human Behav. 63, 433\u2013443 (2016)","journal-title":"Comput. Human Behav."},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., Vakali, A.: Mean birds: detecting aggression and bullying on Twitter. In: Proceedings of the 2017 ACM on Web Science Conference, New York, USA, pp. 13\u201322 (2017)","DOI":"10.1145\/3091478.3091487"},{"key":"15_CR17","unstructured":"Dadvar, M., de Jong, F., Ordelman, R., Trieschnigg, D.: Improved cyberbullying detection using gender information. In: 12th - Dutch-Belgian Information Retrieval Workshop. DIR\u20192012, pp. 22\u201325 (2012)"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Hosseinmardi, H., Mattson, S.A., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Detection of cyberbullying incidents on the Instagram social network. In: 13th Annual International Conference on Mobile Systems, Applications, and Services, Florence, 18\u201322 May 2015, p. 481. ACM (2015)","DOI":"10.1145\/2742647.2745908"},{"key":"15_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1007\/978-3-319-98192-5_9","volume-title":"The Semantic Web: ESWC 2018 Satellite Events","author":"D Robinson","year":"2018","unstructured":"Robinson, D., Zhang, Z., Tepper, J.: Hate speech detection on Twitter: feature engineering v.s. feature selection. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 11155, pp. 46\u201349. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98192-5_9"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Stillwell, D., Matz, S.: Latent human traits in the language of social media: an open-vocabulary approach latent human traits in the language of social media. PLoS ONE 13(11) (2018)","DOI":"10.1371\/journal.pone.0201703"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Waseem, Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter. In: Proceedings of NAACL-HLT, pp. 88\u201393 (2016)","DOI":"10.18653\/v1\/N16-2013"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 759\u2013760 (2017)","DOI":"10.1145\/3041021.3054223"},{"key":"15_CR23","unstructured":"Tommasel, A., Rodriguez, J.M., Godoy, D.: Textual aggression detection through deep learning. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying, pp. 177\u2013187 (2018)"},{"key":"15_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-319-76941-7_11","volume-title":"Advances in Information Retrieval","author":"S Agrawal","year":"2018","unstructured":"Agrawal, S., Awekar, A.: Deep learning for detecting cyberbullying across multiple social media platforms. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 141\u2013153. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76941-7_11"},{"key":"15_CR25","unstructured":"Dadvar, M., Eckert, K.: Cyberbullying detection in social networks using deep learning based models; a reproducibility study. In: DaWaK, pp. 1\u201313 (2018)"},{"key":"15_CR26","unstructured":"Ranasinghe, T., Zampieri, M., Hettiarachchi, H.: BRUMS at HASOC 2019: deep learning models for multilingual hate speech and offensive language identification. In: FIRE 2019 (2019)"},{"key":"15_CR27","unstructured":"Samghabadi, N.S., Patwa, P., Pykl, S., Mukherjee, P., Das, A., Solorio, T.: Aggression and misogyny detection using BERT: a multi-task approach. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying LREC 2020, pp. 126\u2013131 (2020)"},{"key":"15_CR28","unstructured":"Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of LIWC2015 (2015)"},{"key":"15_CR29","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1111\/j.1467-8640.2012.00460.x","volume":"29","author":"SM Mohammad","year":"2013","unstructured":"Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29, 436\u2013465 (2013)","journal-title":"Comput. Intell."},{"key":"15_CR30","unstructured":"Plutchik, R.: Emotion: a psychoevolutionary synthesis (1980)"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"Rezvan, M., Shalin, V.L., Sheth, A.: A quality type-aware annotated corpus and lexicon for harassment research. In: WebSci 2018, Web Science. ACM (2018)","DOI":"10.1145\/3201064.3201103"},{"key":"15_CR32","unstructured":"Lecun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: NIPS, pp. 396\u2013404 (1990)"},{"key":"15_CR33","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Sig. Process. 45, 2673\u20132681 (1997)","journal-title":"IEEE Trans. Sig. Process."},{"key":"15_CR34","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT 2019, pp. 4171\u20134186 (2019)"}],"container-title":["Communications in Computer and Information Science","Information Management and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-04447-2_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T17:50:03Z","timestamp":1727027403000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-04447-2_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031044465","9783031044472"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-04447-2_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"20 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SIMBig","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual International Conference on Information Management and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"simbig2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/simbig.org\/SIMBig2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"25","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}