Abstract
With the progress of society and the rapid development of computer technology, rumors arise on social media, which seriously affects the social economy. How to detect rumors accurately and rapidly has become one hot research topic. In this paper, a new early rumor detection model is proposed. The aim of this model is to increase the efficiency and the accuracy of rumor detection simultaneously. Specifically, in this model, the input data is firstly refined through account filtering and data standardization, then the BiGRU is used to consider the context relationship, and a reinforcement learning algorithm is applied to detection. Experimental results show that compared with other early rumor detection models (e.g., checkpoints), the accuracy of the proposed model is improved by 0.5% with the same speed, which testifies the effectiveness of this model.
Highlights
Rumors refer to statements that have no corresponding factual basis but are fabricated and promoted through certain means
In today’s highly developed situation of information dissemination media, it can spread quickly through social media, and malicious rumors may affect economy and society significantly. e negative impact of rumors may increase significantly when certain major events occur, such as the traceability of COVID-19 in 2019. is makes people realize that if malicious rumors are not discovered in time, they may continue to cause significant damage, so the timing of their detection is crucial
A German wing Airbus A320 with 150 people on board crashed in Barcelonite, southern France, with no one surviving. e message was retweeted by multiple users on Twitter, either by reposting, commenting, or questioning the original source message
Summary
Rumors refer to statements that have no corresponding factual basis but are fabricated and promoted through certain means. Considering that the research of rumor detection technology on the Weibo dataset has been relatively mature, and the latest accuracy rate has reached about 95%, this paper uses public standard Twitter dataset as the main research object. E method of fixed checkpoints can evaluate the timeliness of the discovery of rumors, but this method has the disadvantage of not being able to capture the changes in different rumors spreading modes On this basis, Farajtabar et al [3] proposed a combination of reinforcement learning and a point process network activity model to detect false news and achieved good results. (2) In rumor detection, aiming at the early rumor detection with checkpoints, this paper proposes a data preprocessing method based on account filtering and text standardization for the first time. (3) In this paper, Q-learning, a reinforcement learning algorithm, is applied for rumor detection to dynamically determine checkpoints, thereby improving the timeliness of rumor detection
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