Detecting and Tracking Rumours in Social Media Based on Deep Learning Algorithm

Abstract

In recent years, with the rise of social media, online rumours have become more widespread and have a wider impact. In the era of social media, more and more network users take photos of themselves or others, actively express their views and opinions, and even interact and communicate with others, which forms online public opinion. The automatic detection technology of rumours can purify the network environment and avoid chaos or turbulence in society. Therefore, this paper proposes a deep-learning algorithm to detect and track rumours in social media. After eliminating some feature types for the real-time detection of data flow, when the time index reaches 40, the real-time accuracy of the data mining algorithm is 57.23%, that of the ant colony algorithm is 53.45%, and that of the deep learning algorithm is 64.58%. It can be seen that the real-time accuracy of the deep learning algorithm in this paper is the highest. The effectiveness of this method is also confirmed. Break through the limitations of traditional skills under the deep learning algorithm, using traditional painting and involving devices, ready-made materials, popular symbols, digital technology and other means.