# 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
There are many advanced technologies applied in group identification and classification. [PERSON] et al. [65] have developed a novel algorithm based on the Forest Fire model. The model used the identified important features to obtain the probability of a node to spread a rumor. By detecting the rumor-impacted nodes in the network, the extent of rumor spread across a network was measured. The proposed method can also depict the propagation of rumors, and the key nodes which played a vital role in spreading the rumor can also be identified. [PERSON] et al. [66] applied NLP techniques to quantify the user's sentiment tendency and recent interests. Then, those results were combined with other personalized features to train an XGBoost classification model, and potential rumor refuters can be identified from various rumor events in aspects of economics, society, disaster, politics, and military. Furthermore, agreement and disagreement views analysis marked with attributes can also be applied in crowd classification by NLP. In addition, some machine learning algorithms, such as LR, RF, SVM, GBDT, XGBoost, are potential tools in group identification and classification. GBDT is one of the best algorithms that fit the true distribution in traditional machine learning algorithms, and it can be used for classification and feature selection. XGBoost is widely used in various fields due to its high accuracy, parallel processing, and portability. In future research, the above algorithms can be applied to the crowd identification and classification of social rumor refutation. Applications of Advanced Analysis Technologies in Rumor Governance Effectiveness/Capabilities Evaluation  
Although people have made efforts to control rumors, there are still widespread rumors, even repeated, intensified with deeper impact. In addition to the rapid development of the Internet and other objective factors, to a large extent, it is due to the lack of refutation effectiveness/capabilities evaluation and failure to grasp key factors to improve the effect of false rumor refutation. [PERSON] et al. [67] proposed a rumor refutation effectiveness index (denoted as REI) to evaluate the effectiveness of a rumor refutation microblog on social media. NLP approaches and other big data analysis tools make it convenient to identify the emotional polarity of the comments, extract the characteristics of the text and measure the sentiment of a rumor refutation microblog. To explore the relationship between REI and its possible affecting factors, conventional linear regression model and three regression models from the machine learning field, i.e., support vector regression model (SVR), extreme gradient boosting regression model (XGBoostRegressor), and light gradient boosting machine regression model (LGBMRegressor), are compared. Based on the model with the best performance, they provided useful suggestions on enhancing the rumor refutation effectiveness. Many advanced technologies, such as multi-attribute decision making, group decision making, fuzzy comprehensive evaluation, have been applied in the evaluation problems in different areas. Not much attention has been paid to false rumor refutation effectiveness/capabilities evaluation, and it can be a potential research direction in the future. Feasible and practical suggestions can be proposed for false rumor refutation participants when we can evaluate their effectiveness/capabilities properly. At the same time, combining the different advantages and disadvantages of different platforms, we can propose a multi-platform collaborative refutation response mechanism and a combination of refutation paths, including:  
(1) The topics of online rumors cover a wide range of fields and involve different public groups. It is supposed to adopt a differentiated multi-subjects combination strategy to gradually integrate the official false rumor refutation force and the private refutation force with the understanding of the advantages and disadvantages of various rumor governance participants.