Papers
2
Total Citations
56
H-Index
2
About
Shujuan Ji is a leading researcher in the field of natural language processing and computational trust, with a primary focus on the detection of deceptive online reviews. Her work addresses the critical challenge of identifying fraudulent or misleading content in e-commerce and social platforms, a task essential for maintaining the integrity of digital marketplaces. Ji’s major contributions lie in developing innovative frameworks that combine coarse-grained and fine-grained linguistic features to improve detection accuracy. Her 2020 paper, "A deceptive review detection framework: Combination of coarse and fine-grained features," has garnered 32 citations, reflecting its influence in shaping modern deception detection methodologies. Building on this, her 2021 study, "A deceptive reviews detection model: Separated training of multi-feature learning and classification," with 24 citations, introduces a novel approach that decouples feature learning from classification, enhancing model robustness and scalability. Ji’s work is notable for its practical applicability, offering tools that can be integrated into real-world review systems to combat misinformation. Her research continues to inspire students and scholars working at the intersection of machine learning, text analysis, and cybersecurity.
Research Focus
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Top Papers
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