Graph Deep Learning for Content Risk Control and APT Detection

This article presents a comprehensive overview of Tencent AI Lab's graph‑based approaches for detecting misinformation and advanced persistent threats, detailing the challenges of modeling news content and social context, the design of the Post‑User Interaction Network (PSIN), experimental results on large multi‑topic datasets, and a novel graph‑pretraining pipeline for APT detection.

APT detectionDeep LearningSocial Network Analysis

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Graph Deep Learning for Content Risk Control and APT Detection

Qunar Tech Salon

Apr 29, 2019 · Artificial Intelligence

Multi‑Level Deep Model Fusion for Fake News Detection Using BERT – Winning Solution of WSDM Cup 2019

The article details the Travel team's award‑winning solution for the WSDM Cup 2019 fake‑news detection task, describing data analysis, preprocessing, label‑propagation augmentation, a BERT‑based baseline, a three‑stage multi‑level model‑fusion framework, experimental results, and future directions.

BERTModel FusionNLP

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Multi‑Level Deep Model Fusion for Fake News Detection Using BERT – Winning Solution of WSDM Cup 2019

Meituan Technology Team

Feb 21, 2019 · Artificial Intelligence

Fake News Detection with Multi‑level BERT Fusion at WSDM Cup 2019

In the WSDM Cup 2019 fake-news detection challenge, the Meituan Travel team secured second place by combining extensive data analysis, Chinese-English BERT fine-tuning, label-propagation augmentation, and a three-level fusion framework—blending, stacking, and linear regression—that lifted weighted accuracy to 0.88156.

BERTModel FusionNLP

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Fake News Detection with Multi‑level BERT Fusion at WSDM Cup 2019