| Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings S Han, R Mao, E Cambria International Conference on Computational Linguistics, 2022 | 71 | 2022 |
| RP-DNN: A tweet level propagation context based deep neural networks for early rumor detection in social media J Gao, S Han, X Song, F Ciravegna arXiv preprint arXiv:2002.12683, 2020 | 38 | 2020 |
| Sentic parser: A graph-based approach to concept extraction for sentiment analysis E Cambria, R Mao, S Han, Q Liu 2022 IEEE International Conference on Data Mining Workshops (ICDMW), 1-8, 2022 | 32 | 2022 |
| Neural language model based training data augmentation for weakly supervised early rumor detection S Han, J Gao, F Ciravegna Proceedings of the 2019 IEEE/ACM International Conference on Advances in …, 2019 | 25 | 2019 |
| Data augmentation for rumor detection using context-sensitive neural language model with large-scale credibility corpus S Han, J Gao, F Ciravegna | 18 | 2019 |
| Rumour Detection on Social Media for Crisis Management. S Han, F Ciravegna ISCRAM, 2019 | 13 | 2019 |
| PrimeNet: A framework for commonsense knowledge representation and reasoning based on conceptual primitives Q Liu, S Han, E Cambria, Y Li, K Kwok Cognitive Computation, 1-28, 2024 | 10 | 2024 |
| A survey on pragmatic processing techniques R Mao, M Ge, S Han, W Li, K He, L Zhu, E Cambria Information Fusion, 102712, 2024 | 6 | 2024 |
| Context-aware message-level rumour detection with weak supervision S Han University of Sheffield, 2020 | 5 | 2020 |
Erik CambriaProfessor of Artificial Intelligence, CCDS, NTU SingaporeDirección de correo verificada de ntu.edu.sg