DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform Murshed, BAH, Abawajy, Jemal, Mallappa, S, Saif, MAN and Al-ariki, HDE 2022, DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform, IEEE Access, vol. 10, pp. 25857-25871, doi: 10.1109/ACCESS.2022.3153675. Attached Files Name Description MIMEType Size Downloads Title DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform Author(s) Murshed, BAH Abawajy, Jemal orcid.org/0000-0001-8962-1222 Mallappa, S Saif, MAN Al-ariki, HDE Journal name IEEE Access Volume number 10 Start page 25857 End page 25871 Total pages 15 Publisher Institute of Electrical and Electronics Engineers (IEEE) Place of publication Piscataway, NJ Publication date 2022-02-23 ISSN 2169-3536 Keyword(s) Cyber-bullying Tweet classification Dolphin Echolocation algorithm Elman recurrent neural networks Short text topic modeling Cyberbullying detection Social media Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering Cyberbullying Blogs Feature extraction Support vector machines Recurrent neural networks Training Numerical models Language eng DOI 10.1109/ACCESS.2022.3153675 Field of Research 08 Information and Computing Sciences 09 Engineering 10 Technology HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30164568 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 18 Mar 2022, 07:24:27 EST Fri, 18 Mar 2022, 12:34:13 EST Fri, 18 Mar 2022, 15:56:56 EST Fri, 18 Mar 2022, 16:12:26 EST Fri, 25 Mar 2022, 13:47:27 EST Fri, 25 Mar 2022, 15:51:46 EST Fri, 25 Mar 2022, 15:56:18 EST Fri, 08 Apr 2022, 18:35:19 EST Wed, 20 Apr 2022, 13:35:22 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 23 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Fri, 18 Mar 2022, 12:34:13 EST