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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM68069

Nexora: An AI-Powered Social Networking Platform for Misinformation Detection, Content Verification, and Trust-Aware Communities

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68069

Volume:

10 |

10 |

Oct 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68069

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.

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Authors

Rajat Dube

Rajat Dube

Computer Technology

Priyadarshini college of engineering

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Rohit Meshram

Rohit Meshram

Computer Technology

Priyadarshini college of engineering

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Tushar Mendhule

Tushar Mendhule

Computer Technology

Priyadarshini college of engineering

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Radhesh Rathod

Radhesh Rathod

Computer Technology

Priyadarshini college of engineering

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Sanket Chopkar

Sanket Chopkar

Computer Technology

Priyadarshini college of engineering

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Mentors

Sandhya Patil

Sandhya Patil

Computer Technology

Priyadarshini college of engineering

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Abstract

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

Artificial Intelligence Social Media Misinformation Detection Fake News Detection AI Generated Content Deepfake Detection Fact Verification Trust Score Trust Labeling Age Assurance.

Cite This Article

Rajat Dube, Rohit Meshram, Tushar Mendhule, Radhesh Rathod, Sanket Chopkar, Sandhya Patil, "Nexora: An AI-Powered Social Networking Platform for Misinformation Detection, Content Verification, and Trust-Aware Communities," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-6, Oct 2026. https://doi.org/10.55041/IJSREM68069

Full Article/Conclusion

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.  

References

[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.

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Publication Date

10/17/2026

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Double-Blind Peer Review

Review Rounds: 2

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