## 5 Conclusions  
Based on the fusion of original text content, propagation text content and the propagation structure, we propose an effective false information detection model called ICP-BGCN that fuses message content with the propagation path. The BERT model is employed to discern the deep semantic features of the original text and the propagation text. The propagation structure is integrated with the semantic features of the text, and the bidirectional graph convolutional neural network is used to learn the propagation features from the semantic in information. The obtained propagation features are combined with the enhanced semantic features of the original text to generate fusion features, upon which false information detection is performed. To evaluate the rationality and effectiveness of our ICP-BGCN model, comparative experiments are conducted on the public datasets Twitter15 and Twitter16. The experimental results show that, in general, our ICP-BGCN model performs better than eight baseline models, such as DTC. To further assess its generalization capability, we validate the model on the Preme dataset, achieving robust performance (84.4% accuracy) that demonstrates cross-domain adaptability. We compare the propagation network structures of disinformation and non-disinformation in the Twitter15 and Twitter16 datasets, and find that the disinformation propagation structure exhibits a low-density hetero-collocation network characterized by multiple nodes with high connectivity. In addition, the Twitter16 dataset shows more obvious characteristics in its disinformation propagation networks compared to the Twitter15 dataset, which partially explains the higher performance of our model over this dataset. This study did not take into account the gradual loss of influence of posts over time, which will be considered in future work to more accurately model the dynamic process of information dissemination. In addition, we will focus on the early stages of disinformation propagation, using incomplete propagation structures and limited information for early disinformation detection.  
Conceptualization, M.Y., J.H. ([PERSON]) and J.H. ([PERSON]); methodology, M.Y.; software, M.Y.; validation, M.Y., B.T. and J.H. ([PERSON]); data curation, M.Y. and B.T.; writing--original draft, M.Y.; writing--review and editing, J.H. ([PERSON]), B.T. and J.H. ([PERSON]); visualization, M.Y., B.T. and J.H. ([PERSON]); supervision, J.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guizhou Provincial Science and Technology Fund (Qian Kehe Basic-ZK [2021] General 337), supported by the Fund of the State Key Laboratory of Public Big Data, Guizhou University (No. PBD2023-35), and the Graduate Program of Guizhou University of Finance and Economics (2022 ZXSY036).  
Not applicable.  
The datasets used in the experiments were based on the two publicly available Twitter15 and Twitter16 datasets released by [PERSON] et al. [38]. The raw datasets can be respectively downloaded from [[https://www.dropbox.com/s/7](https://www.dropbox.com/s/7) ewzdrbelpmmxu/rumdetect2017.zip?dl=0]([https://www.dropbox.com/s/7](https://www.dropbox.com/s/7) ewzdrbelpmmxu/rumdetect2017.zip?dl=0) (accessed on 1 May 2023).  
The authors declare no conflicts of interest.