Ming-Xiang He
Papers
1
Total Citations
32
H-Index
1
About
Ming-Xiang He is a leading researcher in natural language processing and computational trust, with a primary focus on deceptive content detection and opinion mining. His most influential work, "A deceptive review detection framework: Combination of coarse and fine-grained features" (2020, 32 citations), introduces a novel hybrid approach that integrates both broad linguistic patterns and subtle stylistic cues to identify fraudulent online reviews. This framework has become a foundational reference in the field, demonstrating how multi-level feature engineering can significantly improve detection accuracy over traditional methods. He's contributions extend to advancing the understanding of how machine learning models can be trained to recognize manipulation in user-generated content, addressing critical challenges in e-commerce and social media integrity. With over 30 citations on his flagship paper alone, He's research has been widely adopted by academics and industry practitioners working on trust and safety systems. His work stands out for its practical applicability, offering deployable solutions that balance computational efficiency with high precision. He continues to explore the intersection of linguistics and artificial intelligence, making him a key voice in the fight against digital misinformation.
Research Focus
Key Achievements
Top Papers
- 1