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Real-Time Detection

Real-time detection of misinformation refers to identifying and flagging false claims, manipulated content, and coordinated disinformation campaigns as they emerge and propagate on social media platforms. This requires both accuracy and speed, processing large volumes of posts per minute while maintaining low false-positive rates.

Challenges

Volume and velocity: Modern social platforms generate millions of posts per minute. Detection systems must process this volume without significant latency.

Emerging narratives: Misinformation often exploits breaking news and real-world events; detectors must adapt quickly to new false claims without waiting for labeled training data.

Computational cost: Deep learning and large language models are computationally expensive, creating tension with low-latency requirements.

Context gathering: Real-time detection often has limited access to full context (user history, network structure, temporal evolution), forcing decisions on partial information.

Verification delays: Fact-checking and source verification are inherently slow, yet real-time systems cannot wait for authoritative verdicts before alerting users or removing content.

Approaches

Early detection via temporal patterns: Analyzing cascade shapes and propagation patterns to identify misinformation before it saturates the platform.

Feature-light classifiers: Using shallow or linear models that sacrifice accuracy for speed.

Streaming architectures: Designed for continuous, online learning from incoming data.

LLM-based systems: Leveraging fast inference and few-shot reasoning to adapt to emerging narratives without retraining.

Key papers