###### Abstract
The only prior work was from [PERSON] et al., who predicted social media rumor refuters only in the disaster category [18]. This study intends to reveal the features of netizens who are willing to retweet rumor refutations (refuters) without extra incentives when confronting rumor refuting messages and user features, and propose a rumor refuter crowd identification model. Five main rumor refuting microblog categories are considered that can potentially affect social stability: economics, society, disaster, politics, and military [19]. Similarities and differences of rumor refuters in these five categories are compared. Natural Language Processing (NLP) and XGBoost are the main tools in this research. NLP is a subfield of computer science, information engineering, and artificial intelligence, and is concerned with programming computers to process and analyze large amounts of human (natural) languages data [20]. Although NLP is already a mature technology, as far as the authors know, the short text similarities and sentiment analysis have not been well-applied in combating rumors, especially associating them with rumor refuting behaviors. Baidu NLP [21] will be applied to quantify the user microblog content's sentiment (recent sentiment tendency) and similarity with original rumor refuting message (recent interests) as a value between 0 and 1, which can also be viewed as a probability. The higher the value, the higher the probability that the sentiment of the microblog is positive or the microblog content is the same as the rumor refuting message. XGBoost [22] is a relatively new algorithm that has gained popularity due to its accuracy and robustness. XGBoost utilizes boosting, which trains each new instance to emphasize the training instances previously mis-modeled for better classification results. It is a combination of classification and regression trees (CART) [23], but re-defined the objective function with both training loss and complexity of the trees to decrease the chance of overfitting. Thus, XGBoost is a very strong model with high extensibility. In recent years, XGBoost has been widely applied to practical problem solving [24; 25]. [PERSON] et al. have proved XGBoost was found to be the most efficient machine learning method for disaster rumor refuter identification compared with logistic regression, support vector machines, and random forest [18]. Therefore, this paper chooses XGBoost to construct the potential rumor refuters identification model. The main contributions of this paper can be summarized as:  
(1) The focus of social media users (instead of only considering influential individuals) in the rumor refutation process. (2) Feature analysis of rumor refuters for five different categories of rumors, which can provide guidance on the personalized recommendation for social media users by accelerating the rumor refutation information dissemination. (3) XGBoost based identification model to identify the rumor refuters and extract significant features of rumor refuters. The remainder of this paper is organized as follows. Section 2 gives our research motivations. Section 3 shows the methods and results and Section 4 gives the discussion. Section 5 concludes the work and discusses future research applications.