The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
IMPULSIVE INTERMODAL CYBER BULLYING RECOGNITION FROM PUBLIC NETS
Open accessAbstract
Cyberbullying has grown as an important societal challenge nowadays. The Cyberbullying affects both in terms of psychological and emotional means of a person. So there is a need to devise a method to detect and prevent cyberbullying in social networks. Most of the existing cyberbullying methods involves only text detection and few methods are available for analysing the visual detection. In this proposed work is going to detect multimodel cyberbullying such as audio, video, image along with text in the social networks. The cyberbully image will be detected using the computer vision algorithm which includes two methods like Image Similarity and Optical Character Recognition (OCR). The cyberbully video will be detected using the Shot Boundary detection algorithm where the video will be broken into frames and analysed using various methods in it. The proposed framework also support to identify the cyberbully audio in the social network. Finally the cyberbully data will be classified into Physical bullying, Social bullying and Verbal bullying using classifiers.
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“Studies [85,86] proposed a multimodel cyberbullying detection framework where they applied OCR to detect cyberbullying from image data.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [86]
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Cites
Al-Garadi, M. A., Varathan, K. D., & Ravana, S. D. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.051 · Cited by 356
Social Media and Depressive Symptoms in Childhood and Adolescence: A Systematic Review
McCrae, N., Gettings, S., & Purssell, E. (2017) · Adolescent Research Review · doi:10.1007/s40894-017-0053-4 · Cited by 304
Cyber-Extremism: Isis and the Power of Social Media
Awan, I. (2017) · Society · doi:10.1007/s12115-017-0114-0 · Cited by 261
Kim, S., Colwell, S. R., Kata, A., Boyle, M. H., & Georgiades, K. (2018) · Journal of Youth and Adolescence · doi:10.1007/s10964-017-0678-4 · Cited by 162
Online Social Network Bullying Detection Using Intelligence Techniques
Nandhini, B. S., & Sheeba, J. I. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.03.085 · Cited by 115
Continued Bullying Victimization in Adolescents: Maladaptive Schemas as a Mediational Mechanism
Calvete, E., Fernández‐González, L., González‐Cabrera, J. M., & Gámez‐Guadix, M. (2017) · Journal of Youth and Adolescence · doi:10.1007/s10964-017-0677-5 · Cited by 105
Toward Multimodal Cyberbullying Detection
Singh, V. K., Ghosh, S., & Jose, C. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053169 · Cited by 96
Cyberbullying Detection and Classification Using Information Retrieval Algorithm
Nandhini, B. S., & Sheeba, J. I. (2015) · International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET) · doi:10.1145/2743065.2743085 · Cited by 86
Jiménez, S. Y., Navarro, R., Larrañaga, M. E., Rubio, E. L., & Bernal, A. O. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.05.037 · Cited by 70
A Framework for Cyberbullying Detection in Social Network
Kansara, K. B., & Shekokar, N. M. (2015) · Cited by 44
Research works printed but not linked: 2
- Saravanaraj, A., J. I. Sheeba, and S. Pradeep Devaneyan. "Automatic Detection of Cyberbullying from Twitter." International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555, Vol.6, No.6, (2016), pp.27-32.
- J.I.Sheeba,TataPrathyusha and R.Hemavathy, "Cyberbully Detection and Classification System Using Hybrid Techniques", In Proceedings of the IEEE International Conference on International Conference On Telecommunication, Power Analysis And Computing Techniques (ICTPACT 2017),April 6-8,Tamil Nadu, India.
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Sorted by citations
A Review on Deep-Learning-Based Cyberbullying Detection
Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103
“Studies [85,86] proposed a multimodel cyberbullying detection framework where they applied OCR to detect cyberbullying from image data.”
Method
Metadata
- Authors
- PG Student, D. O. C. S. A. E. P. E. C. P., & T, P.
- Venue
- International Journal of Advanced Research in Computer Science
- Published
- 2018-06-20
- Volume
- 9
- Issue
- 3
- Pages
- 59-63
- ISSN
- 0976-5697 (ISSN-L)
- OpenAlex
- W2809880223
- Cited by
- 2
- References
- 16
- Citations by year
- 2023: 1 · 2022: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Advanced Research in Computer Science (publishedVersion, cc-by)
- International Journal of Advanced Research in Computer Science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Psychology › Social Psychology › Bullying, Victimization, and Aggression
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- cyberbullying detection, social networks, image similarity, optical character recognition, shot boundary detection, verbal bullying, social bullying, physical bullying
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