# Applications of Advanced Analysis Technologies in Precise Governance of Social Media Rumors
## 4 Applications of Advanced Analysis Technologies in Social Media Rumor Precise Governance
### Applications of Advanced Analysis Technologies in Crowd Identification and Classification  
[PERSON] et al. believed that the same refutation information should be rewritten in different statements and perspectives according to different people's characteristics and thinking patterns, avoiding sensitive points such as political stand and world view [64]. This requires crowd classification of the public, that is, the target of rumor governance. In addition, the group prediction model can also be used to predict potential false rumor spreaders. From this perspective, it is possible to distinguish the opinions and information expression preferences of different groups of people and to adopt rumor refutation decisions for different groups. This can break through the dilemma of receiving refutation information and realize the effective dissemination of refutation information. For more details:  
Based on the group classification model, the participants of rumor governance can push the popular science knowledge or policy popularization information to the past rumors participants according to the crowd classification results. For example, for the former false rumor spreaders, push the corresponding type of science information and remind them to pay attention to content review when publishing information. Secondly, for those who did not believe in false rumors but have not yet become active rumor refuters, push platform incentive policies information, such as rumor refutation rewards points. Besides, for false rumor refuters, the platform can also push incentive policies information and other types of refutation information that they are interested in, encouraging them to continue to be active science popularizers and purifiers on the Internet. If their reposts cause a certain influence, the platform should honor the incentives in time, such as gift points, virtual gifts, membership level upgrades. In this way, a false rumor refutation platform can establish a long-term incentive mechanism that can advocate folk rumor refutation, reward science popularization, and promote continuous emergence of science popularization talents. Lastly, for rumor believers, the platform should regularly push the same type of science popularization information to carry out science popularization education for them to avoid being confused by similar false rumors. From the follows and tags of the rumor participants, platforms can mine the preferences of different groups for different media and try to push targeted information through the media that they are interested in and trust. In addition, the platform can push refutation information of false rumors corresponding to the interest preferences of different groups. According to the group prediction model, when a false rumor appears or is in the process of spreading, platforms can predict who among the public may become rumor refuters, rumor spreaders, rumor believers, and unbelievers. Then relevant popular science information can be early pushed, trying to make science popularization or rumor refutation repeatedly before the rumor information reaches them to achieve \"rumor immunity\". Furthermore, for participants of different types of rumors, personality analysis can be conducted from the information and comments that they have proactively released recently. These participants can be further classified in detail so that the pushed information can be revised to suit their personalities. Combination of multiple information forms. Information on social media networks is composed of various forms, mainly including text, pictures, video, voice, etc. The multiple expression forms increase the expressive power of the information. The dissemination of refutation information should make full use of the group's preference for the information types and design the most acceptable information forms for the group so that the masses will eventually accept the refutation content and science information content. This helps to form the correct information perspective to improve the acceptance of information.