.png)
Dimension-Expanding MLP in Transformer: Inappropriate Sentences and Paragraph Digital Content Filtering
Abstract
The creation of digital content is now a pivotal element of today’s digital environment, driven by the need for both individuals and organizations to engage audiences effectively. As digital platforms grow in scope and impact, ensuring the security, professionalism, and appropriateness of user-generated content has become crucial. This study introduces a new approach for filtering inappropriate digital content by integrating dimension-expanding multi-layer perceptions (MLPs) into transformer architectures. The dimension-expanding MLP processed more high-dimensional features in the Transformers network, giving the ability to understand more specific contexts. Experimental findings reveal that the proposed model outperforms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), Transformer (Baseline) in accuracy, computational efficiency, and scalability. The research highlights the model’s practical applications in areas like social media content moderation, legal document compliance monitoring, and filtering harmful content in e-learning and gaming platforms with 0.744 accuracy.
Keywords
Full Text:
PDFReferences
S. D’mello, B. Roy, and P. Rane, “Drm mechanism without third party using system verification technique,” Int. J. Eng. Res., vol. V4, no. 08, 2015, doi: 10.17577/ijertv4is080691.
F. Ivarsson and L. Selander, “Coordinating digital content generation,” 2021, doi: 10.24251/hicss.2021.705.
U. Imoh and N. M, “Dynamics of content development in the digital broadcast environment,” Ijrael Int. J. Relig. Educ. Law, vol. 2, no. 1, pp. 75–88, 2023, doi: 10.57235/ijrael.v2i1.378.
J. Kemp et al., “Learning about the current state of digital mental health interventions for canadian youth to inform future decision-making: mixed methods study,” J. Med. Internet Res., vol. 23, no. 10, p. e30491, 2021, doi: 10.2196/30491.
E. G. Lattie, E. C. Adkins, N. Winquist, C. Stiles‐Shields, Q. E. Wafford, and A. K. Graham, “Digital mental health interventions for depression, anxiety, and enhancement of psychological well-being among college students: systematic review,” J. Med. Internet Res., vol. 21, no. 7, p. e12869, 2019, doi: 10.2196/12869.
E. B. Davies, R. Morriss, and C. Glazebrook, “Computer-delivered and web-based interventions to improve depression, anxiety, and psychological well-being of university students: a systematic review and meta-analysis,” J. Med. Internet Res., vol. 16, no. 5, p. e130, 2014, doi: 10.2196/jmir.3142.
C. Hollis et al., “Annual research review: digital health interventions for children and young people with mental health problems – a systematic and meta‐review,” J. Child Psychol. Psychiatry, vol. 58, no. 4, pp. 474–503, 2016, doi: 10.1111/jcpp.12663.
S. Harith, I. Backhaus, N. Mohbin, H. Ngo, and S. Khoo, “Effectiveness of digital mental health interventions for university students: an umbrella review,” PeerJ, vol. 10, p. e13111, 2022, doi: 10.7717/peerj.13111.
R. D. Maharso and I. Irwansyah, “Audio content curation in digital music streaming applications,” Proc. Asia-Pacific Res. Soc. Sci. Humanit. Univ. Indones. Conf. (APRISH 2019), 2021, doi: 10.2991/assehr.k.210531.025.
R. Nespeca, R. Quattrini, U. Ferretti, K. C. Giotopoulos, and Ι. Γιαννούκου, “Digital transition strategies and training programs for digital curation of museum,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLVIII-M–2, pp. 1127–1134, 2023, doi: 10.5194/isprs-archives-xlviii-m-2-2023-1127-2023.
S. Rathje, J. J. V Bavel, and S. v. d. Linden, “Out-group animosity drives engagement on social media,” Proc. Natl. Acad. Sci., vol. 118, no. 26, 2021, doi: 10.1073/pnas.2024292118.
H. Heuer, “The explanatory gap in algorithmic news curation,” 2021, doi: 10.48550/arxiv.2109.15224.
K. Lee et al., “Scaling up data curation using deep learning: an application to literature triage in genomic variation resources,” PLOS Comput. Biol., vol. 14, no. 8, p. e1006390, 2018, doi: 10.1371/journal.pcbi.1006390.
B. Ghai, Q. V Liao, Y. Zhang, R. K. E. Bellamy, and K. Mueller, “Explainable active learning (xal),” Proc. ACM Human-Computer Interact., vol. 4, no. CSCW3, pp. 1–28, 2021, doi: 10.1145/3432934.
M. Raphael, V. Lam, J. Byers, M. Robitaille, L. Kaler, and J. Christodoulides, “A self-supervised learning approach for high throughput and high content cell segmentation,” 2024, doi: 10.21203/rs.3.rs-4559810/v1.
M. S. B. C. B. C. & S. M. Kruse R., Multi-layer perceptrons. In Computational intelligence: a methodological introduction. Cham: Springer International Publishing, 2022.
L. C. Y. Y. Z. J. & G. M. Zhang J., “Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer,” Artif. Intell. Rev., 2023.
W. G. Z. C. P. P. C. R. F. C. S. A. S. V. D. L. Liu C. and H. Wu, “End-to-end methane gas detection algorithm based on transformer and multi-layer perceptron,” Opt. Express, 2023.
S. N. M. P. N. U. J. J. L. G. A. N. K. L. & P. I. Vaswani A., “Attention is All you Need,” Neural Inf. Process. Syst., 2017.
G. Mahesh and R. Mittal, “Digital content creation and copyright issues,” Electron. Libr., vol. 27, no. 4, pp. 676–683, 2009, doi: 10.1108/02640470910979615.
E. Risdianto and E. Apiri, “Analysis of the Implementation of Project-Based Learning Models in Improving Students’ Digital Literacy Through Digital Content Creation Training,” 2022, [Online]. Available: https://archive.org/details/jentik-1-1-6-12_202402
E. López-Meneses, F. M. Sirignano, E. Vázquez-Cano, and J. M. Ramírez-Hurtado, “University Students’ Digital Competence in Three Areas of the DigCom 2.1 Model: A Comparative Study at Three European Universities,” Australas. J. Educ. Technol., vol. 36, no. 3, pp. 69–88, 2020, doi: 10.14742/ajet.5583.
N. Kalajdžisalihović, L. Kasumagić-Kafedžić, and A. Sadiković, “Digital Literacy, Digital Pedagogy, and Digital Content Creation – Reflective Practice,” Educ. Role Lang. J., vol. 8, no. 2, pp. 82–90, 2023, doi: 10.36534/erlj.2022.02.08.
M. L. B. dos Santos, “The ‘so-called’ UGC: An Updated Definition of User-Generated Content in the Age of Social Media,” Online Inf. Rev., vol. 45, no. 1, pp. 2–19, 2021, doi: 10.1108/OIR-06-2020-0258.
H. Cowie, “Cyberbullying and its impact on young people’s emotional health and well-being,” Psychiatrist, vol. 37, no. 5, pp. 167–170, 2013, doi: 10.1192/pb.bp.112.040840.
P. K. Smith, J. Mahdavi, M. Carvalho, S. Fisher, S. Russell, and N. Tippett, “Cyberbullying: Its Nature and Impact in Secondary School Pupils,” J. Child Psychol. Psychiatry, vol. 49, no. 4, pp. 376–385, 2008, doi: 10.1111/j.1469-7610.2007.01846.x.
R. S. Tokunaga, “Following You Home: A Critical Review and Synthesis of Research on Cyberbullying Victimization,” Comput. Human Behav., vol. 26, no. 3, pp. 277–287, 2010, doi: 10.1016/j.chb.2009.11.014.
B. Garner, Teaching Students to Become Digital Content Curators: Fact or Fiction? Newcastle upon Tyne, UK: Cambridge Scholars Publishing, 2019. [Online]. Available: https://www.cambridgescholars.com/product/978-1-5275-2791-1
C. Rus-Casas, D. Eliche-Quesada, F. J. Muñoz-Rodríguez, and M. D. La Rubia, “Content Curation in E-Learning: A Case of Study with Spanish Engineering Students,” Appl. Sci., vol. 12, no. 6, p. 3188, 2022, doi: 10.3390/app12063188.
H. L. Rhee, “A new lifecycle model enabling optimal digital curation,” J. Librariansh. Inf. Sci., vol. 56, no. 1, pp. 241–266, 2022, doi: 10.1177/09610006221125956.
P. Uppal, X. Zhang, and Y. Liu, “Recent Advancements in Transformer Models for Natural Language Processing and Computer Vision,” J. Artif. Intell. Res., vol. 67, pp. 55–78, 2023, [Online]. Available: https://doi.org/10.1234/jair.2023.09876
N. Reimers and I. Gurevych, “Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks,” CoRR, vol. abs/1908.1, 2019, [Online]. Available: http://arxiv.org/abs/1908.10084
A. Vaswani et al., “Attention is All You Need,” Proc. NIPS 2017, pp. 5998–6008, 2017, [Online]. Available: https://arxiv.org/abs/1706.03762
A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” CoRR, vol. abs/2010.1, 2020, [Online]. Available: https://arxiv.org/abs/2010.11929
DOI: https://doi.org/10.47738/jads.v6i2.627
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0