###### Abstract  
One prominent dark side of online information behavior is the spreading of rumors. The feature analysis and crowd identification of social media rumor refuters based on machine learning methods can shed light on the rumor refutation process. This paper analyzed the association between user features and rumor refuting behavior in five main rumor categories: economics, society, disaster, politics, and military. Natural language processing (NLP) techniques are applied 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 refuters can be identified. Information from 58,807 Sina Weibo users (including their 646,877 microblogs) for the five anti-rumor microblog categories was collected for model training and feature analysis. The results revealed that there were significant differences between rumor stiflers and refuters, as well as between refuters for different categories. Refuters tended to be more active on social media and a large proportion of them gathered in more developed regions. Tweeting history was a vital reference as well, and refuters showed higher interest in topics related with the rumor refuting message. Meanwhile, features such as gender, age, user labels and sentiment tendency also varied between refuters considering categories. rumor refuter; machine learning; nature language processing; XGBoost; feature analysis 2020 10 4711 3390/app10144711 10.3390/app10144711 10.3390/app10144711 10.3390/app10144711 10.3390/app1014471 10. Combating rumors has been a hot research area. A lot of research focuses on the rumor itself--the identification [8], spread [9; 10; 11], and influencing factors of the rumors; while deep-rooted human nature are the main factors for the viral spread of the rumor, that is, people tend to read/share tweets that confirm their existing attitudes (selective exposure), regard information that is more consistent with their pre-existing beliefs as more persuasive (confirmation bias), and prefer entertaining and incredible content (desirability bias) [4]. Existing research on the participants of the rumor is mainly aimed at influential individuals [12] in social networks. At the public level, crowd identification of rumors participants is still worth further study. When people receive a piece of 'news', they may (1) retweet and comment at the same time, or only retweet (spreaders), (2) deny it and spread a corresponding rumor refuting message (refuters), (3) only comment on it or neglect it (stiflers). Individuals' behaviors are closely related with their attitudes [13]. [PERSON] et al. believed that the same rumor refutation information should be changed for different opinions and angles according to the characteristics and thinking patterns of different groups of people, avoiding sensitive positions such as political positions and world views [14]. Therefore, given a rumor category, analyzing the characteristics of voluntary refuters, and identifying the special group from all rumor participants, make it possible to design targeted rumors refutation strategies based on the characteristics and thinking patterns of refuters. The application value is that the platform can consciously recommend rumor refutation information to them, even adapt the information to suit their personality. It is of great significance for expanding the acceptance of real news and suppressing the spread of rumors. Understanding the content of rumor refutation is a re-learning process, with great subjectivity and group differences. Therefore, netizens featuring analysis and crowd identification are critical to breaking through rumor governance difficulty. Recently, the rapid developments in deep learning and machine learning methods make it possible to extract and process large amounts of unstructured social media data [15; 16; 17], so as to identify different crowds and extract group features. This research topic largely remains unexplored.