Abstract
Online social networks provide convenient conditions for the spread of rumors, and false rumors bring great harm to social life. Rumor dissemination is a process, and effective identification of rumors in the early stage of their appearance will reduce the negative impact of false rumors. This paper proposes a novel early rumor detection (ERD) model based on reinforcement learning. In the rumor detection part, a dual-engine rumor detection model based on deep learning is proposed to realize the differential feature extraction of original tweets and their replies. A double self-attention (DSA) mechanism is proposed, which can eliminate data redundancy in sentences and words at the same time. In the reinforcement learning part, an ERD model based on Deep Recurrent Q-Learning Network (DRQN) is proposed, which uses LSTM to learn the state sequence features, and the optimization strategy of the reward function is to take into account the timeliness and accuracy of rumor detection. Experiments show that, compared with existing methods, the ERD model proposed in this paper has a greater improvement in the timeliness and detection rate of rumor detection.
Highlights
With the rapid development of the Internet, social networks and peoples lives have become increasingly close, and the participation and utilization rate of netizens has risen rapidly [1]. e global digital statistics report [2] released by “We Are Social” in 2019 shows that, by the end of 2018, there were 3.48 billion social network users in the world, accounting for 45% of the worlds total population
In terms of rumor detection, this paper first analyzes the existing research on three problems: the inability to obtain the optimal representation of the reply information, the ignorance of the difference between the original tweet and the reply information in the tweet, and the inability to handle redundant data well
In response to the above problems, this paper uses the difference between the original tweet and the reply information in the Twitter data as an entry point and proposes a dual-engine rumor detection module (RDM), which separately deals with the original tweet and the reply information; on the remaining problem, the double self-attention (DSA) mechanism is proposed to solve the problem of data redundancy in the two dimensions of sentences and words
Summary
With the rapid development of the Internet, social networks and peoples lives have become increasingly close, and the participation and utilization rate of netizens has risen rapidly [1]. e global digital statistics report [2] released by “We Are Social” in 2019 shows that, by the end of 2018, there were 3.48 billion social network users in the world, accounting for 45% of the worlds total population. Analyzing the spreading process of this rumor, when there is obvious opposition and questioning information in the comment area, the information can be preliminarily judged as a rumor. Erefore, early rumors detection research on social networks is very important. Aiming at the difference in content characteristics between original tweets and reply messages in Twitter, a dual-engine rumor detection model based on the self-attention mechanism is proposed, which improves the accuracy of rumor detection; in addition, we propose an early rumor detection (ERD) based on recurrent Q-learning, which can detect rumors earlier with higher accuracy. E remainder of this paper is organized as follows: Section 2 briefly introduces the related works and research issues; Section 3 proposes an ERD model based on deep recurrent Q-learning and a dual-engine rumor detection model based on self-attention mechanism; Section 4 discusses and analyzes the experiment results.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
