Joint rumour and stance identification based on semantic and structural information in social networks

Author:

Luo Nanhang,Xie Dongdong,Mo Yiwen,Li Fei,Teng Chong,Ji DonghongORCID

Funder

National Natural Science Foundation of China

Key Technologies Research and Development Program

Research Foundation of Ministry of Education of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference59 articles.

1. DiFonzo N, Bordia P (2007) Rumor, gossip and urban legends. Diogenes 54(1):19–35. https://doi.org/10.1177/0392192107073433

2. Ma J,Gao W, Wong K (2018) Detect rumor and stance jointly by neural 2 multi-task learning. In: Companion of the the web conference 2018 on 3 the web conference 2018, WWW 2018. ACM, Lyon, vol 4, pp 585–593. https://doi.org/10.1145/3184558.3188729

3. Zhu J,Li J,Zhu M,Qian L,Zhang M,Zhou G (2019) Modeling graph structure in transformer for better amr-to-text generation. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019. Association for Computational Linguistics, Hong Kong, pp 5458–5467. https://doi.org/10.18653/v1/D19-1548

4. Yan Z, Zhang C, Fu J, Zhang Q,Wei Z (2021) A partition filter network for joint entity and relation extraction. In: Proceedings of the 2021 conference on empirical methods in natural language processing, EMNLP 2021, virtual event. Association for Computational Linguistics, Punta Cana, pp 185–197. https://doi.org/10.18653/v1/2021.emnlp-main.17

5. Derczynski L, Bontcheva K, Liakata M, Procter R, Hoi GWS, Zubiaga A (2017) Semeval-2017 task 8: rumoureval: determining rumour veracity and support for rumours. In: Proceedings of the 11th international workshop on semantic evaluation, SemEval@ACL 2017. Association for Computational Linguistics, Vancouver, pp 69–76. https://doi.org/10.18653/v1/S17-2006

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