## 3 Methods and Results
### Rumor Refuters Identification
Therefore, for the disaster, economic, political, and societal categories and all samples, the amount of samples (randomly selected each time and not applied to the military category because there were only \begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Variable Descriptions** \\ \hline R & Dependent variable. Whether the user retweeted the rumor refuting microblog. \\  & 1 for yes and 0 for no. \\ G & Gender of the user set. 1 for male and 0 for female. \\ ML & Membership Level. Explains the user devotion and activity to some extent. \\ L & Location. The provincial level location of the user. \\  & 1 if the user was in the same province as the rumor; 0 otherwise. \\ A & Age. The age of the users. If the value was missing, we interpolated the average age in \\  & & that category. \\ LSm & Similarity between the label information and the rumor refuting microblog ranging from 0–1; \\  & the larger the value, the more similar the two texts. \\ VISm & Similarity between the verified information and the rumor refuting microblog ranging from 0–1; the larger the value, the more similar the two texts. \\ SSE & Sentiment of the Signature or a Brief Introduction of the user ranging from 0–1; the larger the value, the more positive the attitude. \\  & & value, the more positive the attitude. \\  & & positive the Signature or Brief Introduction of the user and the rumor refuting microblog ranging from 0–1; the larger the value, the more similar the two texts. \\  & & number of Microblogs the user has already posted. \\ NOU & Number of Users the user has followed. \\  & & number of Followers the user has. \\  & & number of Groups the user has classified as friends. \\  & & Sentiment of the ith microblog of the user ranging from 0–1; the larger the value, the more \\  & & positive the attitude. \\  & & Similarity between the ith microblog of the user and the rumor refuting microblog ranging from 0–1; the larger the value, the more similar the two texts. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variables for refuter identification. 1055 samples) was gradually increased to examine the influence of the number of samples on the classification results. During this process, different samples were applied for robustness testing and to determine the relationship between sample quantity and the F1 Score/AUC Score when the XGBoost model was applied, with the overall aim being to determine the number of samples needed to obtain a stable, available F1 Score/AUC Score. The feature importance was also ranked using the XGBoost model to determine the most important refuter crowd identification features for the different rumor categories. For those most important features, \(t\)-tests were implemented, to identify which individual feature, for instance, number of microblogs or number of followers, was significantly different between refuters and stiflers. Because the F1 Score and AUC curves were similar, for better observation, only the F1 Score curve was drawn. As it is shown in Figure 4, starting with 500 randomly selected samples, the F1 Score was observed to gradually increase and then, as sample quantity increased in all categories, it became stable at around 0.75 (except for the military category that had only 1055 samples and F1 and AUC scores of 0.65). Even when all sample types were included, the observations remained the same. Therefore, it was concluded that, when plenty of data were provided, the XGBoost model was effective in identifying rumor refuters irrespective of the rumor category differences.