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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68069
Volume:
10 |
10 |
Oct 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Computer Technology
Priyadarshini college of engineering

Computer Technology
Priyadarshini college of engineering

Computer Technology
Priyadarshini college of engineering

Computer Technology
Priyadarshini college of engineering

Computer Technology
Priyadarshini college of engineering

Computer Technology
Priyadarshini college of engineering
Social media has changed how people talk to each other, share news, and find information, but the same speed that makes it useful also makes it easy for fake news, AI-generated misinformation, deepfakes, cyberbullying, and other harmful content to spread. Most platforms still lean on user reports and manual moderation, and by the time a moderator actually looks at a flagged post, it may already have reached thousands of people. Nexora is our attempt at solving this by building verification directly into a social networking platform rather than bolting it on as an afterthought. The platform processes text, images, video, audio, and links through appropriate AI-based analysis workflows, checks for AI-generated content and manipulated media, verifies claims against outside sources, and produces a trust score along with a color-coded trust label. Age assurance is also built into registration, so the platform can support safer participation without collecting more personal information than it needs to. On the technical side, Nexora runs on a Flutter front end, a Node.js/Express backend, MongoDB for storage, and a separate AI processing layer that talks to external verification and age-assurance services. The goal is to cut down how far misinformation travels, give users more context about what they are seeing, and build a community where sharing something questionable is a little harder to do by accident. Since the project is still being built, this paper focuses on the system design, methodology, and evaluation plan rather than claiming final numerical results.
KEYWORDS
Nexora combines AI-based misinformation detection, AI-generated-content analysis, third-party fact verification, privacy-preserving age assurance, and trust labeling into a single social platform. The core idea behind the design is to give users useful context about what they are looking at, instead of leaving them with only manual reporting or a plain fake/real label. By connecting content analysis, evidence verification, trust scoring, and human moderation into one pipeline, we are aiming to build a platform that is more transparent and trustworthy than what currently exists. The project is still under development, so the next steps are implementation, integration, testing, and measuring the results against the evaluation plan laid out in this paper.
[1] L. Wu, F. Morstatter, K. M. Carley, and H. Liu, “Misinformation in Social Media: Definition, Manipulation, and Detection,” ACM SIGKDD Explorations Newsletter, vol. 21, no. 2, pp. 80–90, 2019. [2] M. Wittenberg and A. Berinsky, “Labeling AI-Generated Content: Promises, Perils, and Future Directions,” MIT AI Policy Brief, 2023. [3] F. Li and Y. Yang, “Impact of AI-Generated Content Labels on Perceived Accuracy, Message Credibility, and Sharing Intentions for Misinformation,” JMIR Formative Research, vol. 8, e60024, 2024. [4] C. Drolsbach and N. Prollochs, “Characterizing AI-Generated Misinformation on Social Media,” arXiv:2505.10266, 2025. [5] E. Essa, K. Omar, and A. Alqahtani, “Fake News Detection Based on a Hybrid BERT and LightGBM Model,” Complex & Intelligent Systems, vol. 9, 2023. [6] S. Raza, D. Paulen-Patterson, and C. Ding, “Fake News Detection: Comparative Evaluation of BERT-like Models and LLMs with Generative AI-Annotated Data,” arXiv:2412.14276, 2024. [7] A. Malik, M. Kuribayashi, S. M. Abdullahi, and A. N. Khan, “DeepFake Detection for Human Face Images and Videos: A Survey,” IEEE Access, vol. 10, pp. 18757–18775, 2022. [8] T. Wang, X. Liao, K. P. Chow, X. Lin, and Y. Wang, “Deepfake Detection: A Comprehensive Survey from the Reliability Perspective,” ACM Computing Surveys, vol. 57, no. 3, 2024. [9] Y. Yu, R. Ni, Y. Zhao, et al., “MSVT: Multiple Spatiotemporal Views Transformer for DeepFake Video Detection,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 9, 2023. [10] M. S. Rana, M. N. Nobi, B. Murali, and A. H. Sung, “Deepfake Detection: A Systematic Literature Review,” IEEE Access, vol. 10, pp. 25494–25513, 2022.
Publication Date
10/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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