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Conference Publication

Rumour detection via news propagation dynamics and user representation learning

Host Publication: IEEE Data Science Workshop

Authors: T. Do, D. Nguyen and N. Deligiannis

Publication Date: Jul. 2019

Number of Pages: 5


Abstract:

Rumours have existed for a long time and have been known for serious consequences. The rapid growth of social media platforms has multiplied the negative impact of rumours it thus becomes important to early detect them. Many methods have been introduced to detect rumours using the content or the social context of news. However, most existing methods ignore or do not explore effectively the propagation pattern of news in social media, including the sequence of interactions of social media users with news across time. In this work, we propose a novel method for rumour detection based on deep learning. Our method leverages the propagation process of the news by learning the users' representation and the temporal interrelation of users' responses. Experiments conducted on Twitter and Weibo datasets demonstrate the state-of-the-art performance of the proposed method.

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Mr. Tien Do

+32 (0)2 629 2930

thdo@etrovub.be

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Prof. Dr. Ir. Nikolaos Deligiannis

+32 (0)02 629 168

ndeligia@etrovub.be

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