@inproceedings{75103baed43a48f08573d0d7a40bf4a6,
title = "Deep structure learning for rumor detection on Twitter",
abstract = "With the development of social media and the popularity of mobile devices, it becomes increasingly easy to post rumors and spread rumors on social media. Widespread rumors may cause public panic and negative impact on individuals, which makes the automatic detection of rumors become necessary. Most existing methods for automatic rumor detection focus on modeling features related to contents, users and propagation patterns based on feature engineering, but few work consider the existence of graph structural information in the user behavior. In this paper, we propose a model that leverages graph convolutional networks to capture user behavior effectively for rumor detection. Our model is composed of three modules: 1) a user encoder that models users attributes and behaviors based on graph convolutional networks to obtain user representation; 2) a propagation tree encoder, which encodes the structure of the rumor propagation tree as a vector with bridging the content semantics and propagation clues; 3) an integrator that integrates the output of the above modules to identify rumors. Experimental results on two public Twitter datasets show that our model achieves much better performance than the state-of-the-art methods.",
keywords = "graph convolutional networks, rumor detection, user embedding",
author = "Qi Huang and Chuan Zhou and Jia Wu and Mingwen Wang and Bin Wang",
year = "2019",
doi = "10.1109/IJCNN.2019.8852468",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "1--8",
booktitle = "2019 International Joint Conference on Neural Networks, IJCNN 2019",
address = "United States",
note = "2019 International Joint Conference on Neural Networks, IJCNN 2019 ; Conference date: 14-07-2019 Through 19-07-2019",
}