## 3 Methods and Results
### Feature Analysis between Refuters and Stiflers  
The XGBoost model also provided feature importance rankings (see Figure 5). Except for the disaster and political categories, gender was found to be the least important feature in the XGBoost classifications. However, the MSe11 and MSm11 (regard the topping microblog as the 1 st microblog, and MSe11 and MSm11 refer to the sentiment value and similarity with the origin rumor refuting microblog of the 10 th most recent microblog respectively) appeared to have the most important features for all categories. Figure 4: Refuter classification results based on XGBoost. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **Military** & **Disaster** & **Politics** & **Economics** & **Society** & **All** \\ \hline AUC & 0.6501 & 0.7404 & 0.7350 & 0.7356 & 0.7304 & 0.7356 \\ F1 Score & 0.6501 & 0.7488 & 0.7470 & 0.7511 & 0.7446 & 0.7490 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AUC and F1 Scores for each category and all samples. It was therefore proposed that, if the user had the topping microblog and an emotional inclination and similarity to the original microblog, there would be an influence on the classification judgement. Therefore, samples with MSe11 and MSm11 not equal to 0.5 (i.e., samples with topping microblog) were extracted and their Mse1 and MSm1 were tested and the results are shown in Table 4. However, there were no significant differences between the refuter and stiffer values for their MSe1 and MSm1 in the political-related, disaster-related and military-related categories. The economics-related and societal-related rumor refuter values for MSm1 were lower than those of the stiffers. There were no significant differences in the MSe1 values between the stiffers and refuters in the economics-related category but, for the societal-related category, the refuters' MSe1 values were higher than those of the stiffers. The NOM and NOF were also found to be very important features. The \(t\)-test results in Table 4 found that the rumor refuter NOM and NOF values were somewhat higher than those for the stiffers, which indicated that refuters could be more active. The ML (another measurement of user activity) was also somewhat higher for the refuters in the economics, politics, and societal categories. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Economic** & **Military** & **Societal** & **Disaster** & **Political** \\ \hline ML & **0.000** & **0.000** & **0.000** & **0.000** & **0.000** \\ NOM & **0.000** & **0.000** & **0.000** & **0.000** & **0.000** \\ NOF & **0.000** & **0.003** & **0.000** & **0.000** & **0.000** \\ VISm & **0.000** & **0.001** & **0.000** & **0.000** & **0.000** \\ LSm & **0.000** & **0.033** & 0.335 & **0.000** & **0.000** \\ SSm & **0.000** & **0.000** & **0.000** & **0.027** & 0.815 \\ MSe1 & 0.501 & 0.526 & **0.001** & 0.792 & 0.215 \\ MSm1 & **0.023** & 0.738 & **0.001** & 0.540 & 0.439 \\ MSe & **0.001** & 0.526 & 0.512 & 0.818 & 0.240 \\ MSm & **0.000** & **0.003** & **0.000** & **0.000** & **0.000** \\ \hline \hline \end{tabular} Note: (the value is shown in bold if there were significant differences between the refuters and stiffers under a 95% confidence level, \" \" means that the refuter value was lower than that of the stiffers; for users with a topping microblog, the MSe1 and MSm1 refuter and stiffer values were compared.).  
\end{table} 
Table 4: \(T\)-test results for the rumor refuters and stiffers. Figure 5: Feature importance in the different categories. Except for the LSm in the societal category and the SSm in the political category, there were significant differences found for the VISm, LSm, and SSm between the refuters and stiflers at a 95% confidence level. For the economic, disaster, and military categories, the VISm and LSm of the refuters were significantly lower than those of the stiflers, while the refuters had higher SSm values.