Abstract
Rumor detection is an important research topic in social networks, and lots of rumor detection models are proposed in recent years. For the rumor detection task, structural information in a conversation can be used to extract effective features. However, many existing rumor detection models focus on local structural features while the global structural features between the source tweet and its replies are not effectively used. To make full use of global structural features and content information, we propose Source-Replies relation Graph (SR-graph) for each conversation, in which every node denotes a tweet, its node feature is weighted word vectors, and edges denote the interaction between tweets. Based on SR-graphs, we propose an Ensemble Graph Convolutional Neural Net with a Nodes Proportion Allocation Mechanism (EGCN) for the rumor detection task. In experiments, we first verify that the extracted structural features are effective, and then we show the effects of different word-embedding dimensions on multiple test indices. Moreover, we show that our proposed EGCN model is comparable or even better than the current state-of-art machine learning models.
Highlights
For the first time in history, people consume more news from social media than from traditional sources; people tend to believe the information from the media, making them vulnerable to rumors and fake news [1]
2) To build an effective deep learning model for the rumor detection task, we propose an EGCN model based on a Nodes Proportion Allocation Mechanism (NPAM)
3) To obtain a satisfactory EGCN model for the rumor detection task, we explore the optimal values of the VOLUME 9, 2021 dimension of word vectors
Summary
For the first time in history, people consume more news from social media than from traditional sources (e.g., television, newspapers); people tend to believe the information from the media, making them vulnerable to rumors and fake news [1]. Zhao et al [5] assumes that rumors will cause Twitter users to question the veracity of tweets. This method focuses on content information, but not all rumors provoke inquiry tweets. To make full use of content information, scholars propose some content-based rumor detection models, such as Random Forests (RFs) [6], TF-IDF based models [7], and deep
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