News and Social Media Automatic Detection System based on Big Data Automatic Identification Technology

With the development of the Internet, the number of fake news is extremely large every day, so it is not enough to rely on humans to identify false information. Therefore, the research and application of automatic detection systems based on big data automatic identification technology (BDAIT) is imperative. Fake news and social media information have a strong influence. If people misbelieve and spread it, it will cause misunderstandings and negative emotions from the masses, and it will affect social stability. Therefore, the purpose of this article is to study news and social media automatic detection systems based on BDAIT, assist news organizations and media platforms in identifying fake news in a timely manner, prevent the spread of fake news, reduce its influence, and maintain social stability, the purpose of promoting the healthy development of social networks. Based on the BDAIT, this paper analyzes the requirements based on the current application background, determines the use role and use cases of the system, and then designs the main functions of the system in detail, and finally realizes the use of big data according to the system design. The automatic identification technology is the core of the false news social media automatic detection system. Experiments show that the news and social media automatic detection system based on big data automatic recognition technology uses multi-core asynchronous long CNN to extract the external information features of news. The model parameters converge faster, which is better for the detection effect of the model. After the introduction of Bi-LSTM with attention weight matrix, the system has enhanced the ability of text feature extraction and improved the overall index of the model.