CRCLOct 29, 2025

FakeZero: Real-Time, Privacy-Preserving Misinformation Detection for Facebook and X

arXiv:2510.25932v21 citationsh-index: 4TrustCom
Originality Incremental advance
AI Analysis

This provides a privacy-preserving tool for users and policymakers to combat misinformation on platforms like Facebook and X, though it is incremental as it builds on existing Transformer methods.

The paper tackles the problem of misinformation spread on social media by developing FakeZero, a client-side browser extension that detects unreliable posts in real-time, achieving up to 97.1% macro-F1 and 97.4% accuracy with a median latency of 103 ms on a commodity laptop.

Social platforms distribute information at unprecedented speed, which in turn accelerates the spread of misinformation and threatens public discourse. We present FakeZero, a fully client-side, cross-platform browser extension that flags unreliable posts on Facebook and X (formerly Twitter) while the user scrolls. All computation, DOM scraping, tokenization, Transformer inference, and UI rendering run locally through the Chromium messaging API, so no personal data leaves the device. FakeZero employs a three-stage training curriculum: baseline fine-tuning and domain-adaptive training enhanced with focal loss, adversarial augmentation, and post-training quantization. Evaluated on a dataset of 239,000 posts, the DistilBERT-Quant model (67.6 MB) reaches 97.1% macro-F1, 97.4% accuracy, and an AUROC of 0.996, with a median latency of approximately 103 ms on a commodity laptop. A memory-efficient TinyBERT-Quant variant retains 95.7% macro-F1 and 96.1% accuracy while shrinking the model to 14.7 MB and lowering latency to approximately 40 ms, showing that high-quality fake-news detection is feasible under tight resource budgets with only modest performance loss. By providing inline credibility cues, the extension can serve as a valuable tool for policymakers seeking to curb the spread of misinformation across social networks. With user consent, FakeZero also opens the door for researchers to collect large-scale datasets of fake news in the wild, enabling deeper analysis and the development of more robust detection techniques.

Foundations

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