## 3 Methods and Results
### Data Collection  
Sina Weibo is a Chinese microblogging (Weibo) website and is one of the most popular social media platforms in China with 431 million active monthly users in 2019. Different from Wechat, which only allows a user to post to certified friends, Sina Weibo is the Chinese equivalent of Twitter as it has a wider, more open dispersal. Therefore, crawling microblogs on Sina Weibo has a high research value for rumor propagation or rumor refutation spread analyses. All of the anti-rumor microblogs with a retweet/comment amount larger than 100 were collected from October 2018 to April 2019 using a web crawler.  
This paper only takes the anti-rumor microblogs verified, confirmed, and announced by official accounts (police accounts, government agency accounts, and authoritative media accounts) into considerations. Therefore, the refuters discussed in this paper are those who deliberately spread official accounts' rumor refutation information. As shown in Figure 2, the collected anti-rumor microblogs were classified into five categories based on content [18]; economics, society, disaster, politics, the military, all of which were the common rumors on social media platforms and could result in societal damage. The economic category contained business and entrepreneurial information; the society category covered rumors about social public affairs; the disaster category consisted of distorted information on natural and man-made disasters; the politics category comprised false political messages mainly involving certain political figures, groups, or specific policies; and the military category included rumors about national defense or military affairs.  
Figure 1: The overall framework of the methods.  
These five main categories had a total of 106 anti-rumor microblogs, of which 45 were related with the society, 31 with economics, three with the military, 20 with politics and seven with disaster, with a total of 58,807 user samples. There were far more stiflers than refuters collected because the task of identifying the refuters from the population was inherently an imbalanced classification problem. As this research was simulating the refuter identification process and examining the validity of XGBoost model, testing on a small data subset was considered powerful enough to examine the algorithm's performance [31].  
The users' most recent concerns were strongly associated with their most recent microblogs and our previous work found that the 11 most recently posted microblogs (topping microblogs are included) were reliable predictors in disaster rumor refuter prediction [18]. Except for a few users who had less than 11 microblogs since registration, 11 microblogs were extracted, i.e., the topping microblog (the sticky microblog) and 10 most recently posted microblogs. Although the topping microblogs might not have been recently posted, they were able to reveal the overall attitude of the users to some extent. Basic user information; gender, membership level, location, birthday, verified information, signature or brief introduction, user label, microblog number, number of followers and numbers following, and group numbers for each user; were extracted.  
As the aim of this research was to identify the social media rumor refuter features, information was mined for two groups of people: refuters from the retweet lists and stiflers from the comment lists. Stiflers consist of the commenters and the users who only view the rumor refuting message. Due to the inaccessibility of the viewer list, only those commenters were treated as stiflers. Although both of refuters and stiflers had viewed the rumor refuting microblogs, the responses were quite different.