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A Rumor Detection Method Incorporating Correlation Features

Most of the rumors on social media (e.g., Twitter) revolve around a certain domain or topic, which means that they want to express roughly the same meaning, only the way of expression or the words used are different. Most of the existing rumor detection work focuses on text semantic features, user information features, comment features, and communication structure features, but rarely utilizes the rumor features associated with the original text. Therefore, a GCN-BERT -based rumor detection method is proposed in this paper, which not only utilizes the rumor features of a message itself but also utilizes the rumor features of all the texts and words associated with it when performing rumor detection. Finally, three Twitter datasets are used for comparison experiments, and the results fully demonstrate that the method is more effective than existing rumor detection methods.