## 4 Discussion  
Based on feature analysis of users with different social media behavior, this study sought to identify the potential voluntary rumor refuter, and utilize them with the anti-rumor countermeasure: truth propagation and targeted immunization. Because of the growing popularity of social media and the availability of complete user information, it is possible to accurately obtain user features and therefore easier to identify the potential refuters. Thus, personalized recommendation services could be provided to trigger the spread of the truth, and thus enhance rumor refutation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Disaster** & **Economic** & **Military** & **Politics** & **Society** \\ \hline Refuters in the same province & 224 & 39 & 0 & 42 & 498 \\ Stiflers in the same province & 815 & 210 & 0 & 66 & 1597 \\ Refuter proportion in the same province & 21.56\% & 15.67\% & - & 38.89\% & 23.77\% \\ Refuters not in the same province & 1190 & 4062 & 224 & 2467 & 7466 \\ Stiflers not in the same province & 2209 & 11,697 & 831 & 5513 & 19,657 \\ Refuter proportion not in the same province & 35.01\% & 25.77\% & 21.23\% & 30.91\% & 27.53\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Refuter proportions in the same province as the anti-rumor microblogs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Degree of Freedom** & **Chi-Value** & **\(p\)-Value** \\ \hline Economic & 1 & 12.6312 & 0.0004 \\ Societal & 1 & 13.6454 & 0.0002 \\ Disaster & 1 & 65.7010 & 0 \\ Politic & 1 & 2.8048 & 0.094 \\ Category \& Behavior & 4 & 142.3592 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Chi-square test of contingency results between user behavior and user location. Although previous works have explored the features of retweeters, there have been few studies on utilizing these findings to combat rumor spread. This paper extended the scope of current studies, instead of studying the general features of retweeters or the opposite group, rumor spreaders, it focused on refuters and specified them with five main rumor categories that can affect social stability. Although both rumor spreaders and rumor refuters have the same behavior--retweet, their features were different. In contrast with the conclusion of [PERSON] et al. [4], in which the rumor spreaders were found to have less followers, it was observed that the rumor refuters had a greater number of microblogs and followers; i.e., they were more active. However, this result could be partially explained by [PERSON] et al. [35] that social relationship and message content were noticeable driven factors of retweeting behavior. Our findings were also in line with the literature indicating that users mainly retweet to remind others and express themselves, and retweetability is closely related to the number of followers and tweet contents' information and value [36]. It can be recognized that, when user got more followers, they tended to be more cautious with their microblog contents. Thus, retweeted messages that seem more reliable, and rumor refuting messages released by authoritative media could be one of those. Except for the economic category, there were no significant general sentiment tendency differences between stifiers and refuters. However, the microblog contents and signature contents (except for the political type) of refuters got higher similarity with the original rumor refuting message, and this result was consistent with [PERSON] et al. [29] and [PERSON] and [PERSON] [37]. On the contrary, the similarity between rumor refuting message and verified information and user label were generally lower for refuters (except for the political type), which indicated that the circles and occupations of users were not seriously constant with their daily interests on the social media. Meanwhile, refuters tended to gather in more developed regions. There were specific rumor refuter feature variations in the microblog categories that had not been previously detected.