A Comprehensive Review- Building A Secure Social Media Environment for Kids- Automated Content Filtering with Biometric Feedback

Authors

  • Pandya Vishal Kishorchandra Assistant Professor, Department of Computer Science, Shri V J Modha College of IT, Porbandar, India
  • Vadher B Students, Department of Computer Science, Shri V J Modha College of IT, Porbandar, India
  • Meghnathi R Students, Department of Computer Science, Shri V J Modha College of IT, Porbandar, India
  • Raychura M Students, Department of Computer Science, Shri V J Modha College of IT, Porbandar, India
  • Keshwala K. Students, Department of Computer Science, Shri V J Modha College of IT, Porbandar, India

Keywords:

Secure Social Media, Screen Time Management, Psychological Responses, Automated Content Filtering Using AI, Human-Computer Interaction.

Abstract

This review paper is all about how important it is to use smart technology to keep kids safe on social media while helping them learn better. By adding things like better controls for parents, filters that stop bad stuff, and tools that check how kids are feeling, we can make sure they don't run into anything harmful online. In today's world where kids spend a lot of time online, it's super important to make sure they're safe. If social media platforms start using cool new tech like biometric sensors and wearable gadgets, they can create safer spaces for kids to have fun and learn. This paper also talks about why we need to do things ahead of time to deal with problems like spending too much time on screens or seeing things that might not be right for us. By giving practical ideas for researchers, people who make rules, and companies, this paper wants to make sure kids can enjoy the good parts of social media without any worries.

References

S. J. Kanjibhai and D. P. K. Gokani, “Effective Role of E-governance in Higher Education,” NOLEGEIN-Journal Corp. Bus. Laws, vol. 3, no. 1, pp. 1–6, Jul. 2020, Accessed: Mar. 14, 2024. Available from: https://mbajournals.in/index.php/JoCBL/article/view/507

Z. Thakrar and A. Gonsai, “Combined Study of Oceanography and Indigenous Method for Effective Fishing,” in Proceedings of Second International Conference in Mechanical and Energy Technology: ICMET 2021, India, Springer, 2022, pp. 147–155. Available from: http://dx.doi.org/10.1007/978-981-19-0108-9_16

N. Mehta, “Fuzzy Logic Driven Nutrition-based Recommendation System for Gujarati Cardiac Patients: Integrating Cultural Preferences and Patient Feedback,” J. Comput. Technol. Appl., vol. 15, no. 1, pp. 59-83p, 2024. http://dx.doi.org/10.37591/JoCTA

B. Yu, M. Funk, J. Hu, Q. Wang, and L. Feijs, “Biofeedback for everyday stress management: A systematic review,” Front. ICT, vol. 5, no. SEP, 2018, http://dx.doi.org/10.3389/fict.2018.00023.

N. Mehta and H. Thaker, “Study of Nutrition-Based Recommender System for Diabetes and Cardiovascular Patients Based on Various Machine Learning Techniques: A Systematic Review,” Adv. Inf. Commun. Technol. Comput. Proc. AICTC 2022, pp. 317–327, 2023. https://doi.org/10.1109/ACCESS.2019.2929413

“Sickness Of Scrolling - Google Forms.” https://doi.org/10.1007%2Fs11482-022-10110-7

J. K. Salet and P. K. Gokani, “E-governance: Enhanced Learning Technology in Education Policy”. https://doi.org10.1504/EG.2004.005907

D. Girardi, F. Lanubile, and N. Novielli, “Emotion Detection Using Noninvasive Low Cost Sensors.” https://doi.org/10.1109/ACII.2017.8273589

N. Mehta and H. Thaker, “Data Collection for a Machine Learning Model to Suggest Gujarati Recipes to Cardiac Patients Using Gujarati Food and Fruit with Nutritive Values,” in International Conference on Information and Communication Technology for Intelligent Systems, Springer, 2023, pp. 271–281. https://doi.org/10.31782/IJCRR.2021.13614

D. Link, B. Hellingrath, and J. Ling, “A Human-is-the-Loop Approach for Semi-Automated Content Moderation.” https://doi.org/10.4018/IJISCRAM.2016040103

S. Alghowinem, “A safer YouTube kids: An extra layer of content filtering using automated multimodal analysis,” in Advances in Intelligent Systems and Computing, Springer Verlag, 2018, pp. 294–308. doi: 10.1007/978-3-030-01054-6_21.https://doi.org/10.1007/978-3-030-01054-6_21

C. Conati, R. Chabbal, and H. Maclaren, “A Study on Using Biometric Sensors for Monitoring User Emotions in Educational Games.” https://doi.org/10.1109/GCCE.2018.8574758

R. Gorwa, R. Binns, and C. Katzenbach, “Algorithmic content moderation: Technical and political challenges in the automation of platform governance,” Big Data Soc., vol. 7, no. 1, Jan. 2020, https://doi.org/10.1177/2053951719897945.

S. K. Muppalla, S. Vuppalapati, A. Reddy Pulliahgaru, and H. Sreenivasulu, “Effects of Excessive Screen Time on Child Development: An Updated Review and Strategies for Management,” Cureus, Jun. 2023, https://doi.org/10.7759/cureus.40608.

J. Chaki, N. Dey, F. Shi, and R. Simon Sherratt, “Pattern Mining Approaches Used in Sensor-Based Biometric Recognition: A Review,” IEEE Sens. J., vol. 19, no. 10, pp. 3569–3580, May 2019, https://doi.org/ 10.1109/JSEN.2019.2894972.

A. Brügger, S. Dessai, P. Devine-Wright, T. A. Morton, and N. F. Pidgeon, “Psychological responses to the proximity of climate change,” Nature Climate Change, vol. 5, no. 12. Nature Publishing Group, pp. 1031–1037, Dec. 2015. https://doi.org/10.1038/nclimate2760.

W. Sanders, J. Parent, R. Forehand, and N. L. Breslend, “The roles of general and technology-related parenting in managing youth screen time,” J. Fam. Psychol., vol. 30, no. 5, pp. 641–646, Jan. 2016, https://doi.org/10.1037/fam0000175.

J. Herlocker, “Understanding and improving automated collaborative filtering systems.” https://doi.org/doi/10.5555/932346

Z. Thakrar and A. Gonsai, “Comparing Fish Finding Techniques using Satellite and Indigenous Data based on Different Machine Learning Algorithms,” in Advances in Information Communication Technology and Computing: Proceedings of AICTC 2022, Springer, 2023, pp. 329–340. https://doi.org/10.1016/j.ecolind.2021.107922

J. K. Salet and B. Parekh, “Implementation of E-Governance Framework for Rural Areas of India,” in Advances in Information Communication Technology and Computing: Proceedings of AICTC 2022, Springer, 2023, pp. 341–352.https://doi.org/10.1007/978-981-19-9888-1_26

Z. Thakrar and A. Gonsai, “Predicting Fishing Effort: Data Collection for Machine Learning Model Using Scientific and Indigenous Method,” in International Conference on Information and Communication Technology for Intelligent Systems, Springer, 2023, pp. 207–215. https://doi.org/10.1007/978-981-99-3761-5_20

Z. Thakrar and A. Gonsai, “Design and Development of a Boundary Alert System for Fishermen,” J. Mob. Comput. Commun. Mob. Networks, vol. 11, no. 1, pp. 31-36p, 2024. https://doi.org/10.37591/JoMCCMN

Riad K, Elhoseny M. A New Model for a Secure Social Media Application. Applied Sciences. 2022 Jun 23;12(13):6375. https://doi.org/10.3390/app12136375

Chang, V., Golightly, L., Xu, Q.A., Boonmee, T. and Liu, B.S., 2023. Cybersecurity for children: an investigation into the application of social media. Enterprise Information Systems, 17(11), p.2188122. https://doi.org/10.1080/17517575.2023.2188122

Englehardt, S. and Narayanan, A., 2016, October. Online tracking: A 1-million-site measurement and analysis. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (pp. 1388-1401). https://doi.org/ 10.1145/2976749.2978313

Downloads

Published

2024-07-04

How to Cite

[1]
P. V. Kishorchandra, V. B, M. R, R. M, and K. K., “A Comprehensive Review- Building A Secure Social Media Environment for Kids- Automated Content Filtering with Biometric Feedback”, IJIRCST, vol. 12, no. 4, pp. 25–30, Jul. 2024.

Issue

Vol. 12 No. 4 (2024): July Issue

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.