MetaDetector：Efficient Detection of Fake News
MetaDetector is a fast method for constructing specific fake news detection models in scenarios where there is a scarcity of high-quality training data for newly occurring events and sparse label information.
Model Architecture Diagram：
Description： To address the problem of poor generalization and difficulty in effectively identifying fake news related to new events using existing detection methods. The MetaDetector algorithm utilizes weighted domain adversarial adaptation techniques to transfer event-shared meta-knowledge based on the similarity between source and target events and the transferability between templates and samples, guiding the efficient detection of fake news related to target events.The algorithm takes in a text vector consisting of historical fake news events and newly occurring fake news event posts, and outputs the true or false label of the newly occurring fake news event posts.