# Applications of Advanced Analysis Technologies in Precise Governance of Social Media Rumors
## 4 Applications of Advanced Analysis Technologies in Social Media Rumor Precise Governance  
The development of artificial intelligence and machine learning has made it possible to crawl large amounts of data and process and extract unstructured data. Three different ways can provide the decision-making basis for the precise governance of social media  
Figure 6: The Framework of Social Media Rumor Precise Governance System. rumors: establishing a large database of social media rumors and rumor spreading participants and using advanced big data analysis to extract the characteristics of large-scale spreading and recurring false rumors; accurately identifying and predicting the rumor believers, rumor spreaders, rumor unbelievers and rumor refuters of social media rumors; exploring the factors which have significant impacts on the effectiveness/ability of rumor governance. Applications of Advanced Analysis Technologies in Identification and Monitoring of Large-Scale Spreading and Recurring Social Media False Rumors  
The Internet produces false information, unconfirmed information, and erroneous information all the time. If the rumor governance does not grasp the emphasis, it is easy to fall into the \"ocean\" of information. The focus of governance is the large-scale and recurring false rumors. Whether rumors spread on a large scale was originally a binary classification problem, in order to increase the pertinence of preventive decision-making, a multi-class classification problem of high risk, medium risk, and low risk can be defined in accordance with the spreading risk. Researchers can establish a large database of false social media and use machine learning methods to predict and classify rumors with different spread risk levels. Besides, feature engineering can also be used to screen the characteristics of large-scale spreading false rumors. The characteristics extracted from different types of possible large-scale spreading false rumors provide a basis for the refutation platforms to formulate preventive countermeasures. Knowing the current situational state (variability, danger, sensitivity, local informatization level, etc. ), information verified as false rumors appear in the monitoring system of the rumor refutation platforms. Analyzing its originators and text characteristics and taking these characteristics into the predictive model can quickly predict whether the false rumors will spread on a large scale or the risk level of large-scale spreading. In this way, we can take pertinent preventive measures based on the predicted results and establish a hierarchical early warning system. According to the rumor refutation experience, different levels of emergency refutation plans formed from the aspect of the governance participants, phases, measures, etc., to improve the pertinence and effectiveness of refutation. The recurring false rumors are divided into the following categories according to the repetition cycle: (1) intergenerational rumors--spread across generations, with a rotation cycle of several years; (2) annual ring rumors--recurring during specific regular social activities and time; (3) \"menstrual\" rumors--do not target specific events, but appear at intervals of irregular time throughout the long period; (4) \"influenza\" rumors--similar rumors reappear when similar emergencies (natural disasters, accidents, etc.) occur. It is advisable to explore recurring rumors' characteristics and identify possible recurring rumors based on the rumor database and advanced big data technologies. That is to say, establishing machine learning prediction models and figuring out whether the refutation method, organization, refutation speed, and platforms during rumor's first appearance have a significant impact on the probability of rumor recurring.