## 3 Methods and Results
### Rumor Refuters Identification  
A crowd identification process was applied in two steps. Step 1. Convert the textual content into numerical values. For rumors that are linked to specific geographical locations, we derived the locations from the original rumor texts. Then, comparing the locations where the rumor \"took place\" with the locations each user from, 1 would be assigned for the location feature if the user was in the same province as the rumor, and 0 otherwise. Baidu's AipNLP [21], which is regarded as the most advanced Chinese text analysis technique, was applied to convert the textual content into numerical values. Then, the similarities between the user labels, the verified information, the signature, the most recent 11 microblog (including the topping microblog) contents, and the rumor refuting microblogs were transformed into values between 0 and 1. For the sentiment analysis, the emotional inclinations of the user signatures and the most recent 11 microblog contents were also converted into values between 0 and 1. The processed variables are listed in Table 2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Economics**} & \multicolumn{2}{c}{**Military**} & \multicolumn{2}{c}{**Society**} & \multicolumn{2}{c}{**Disaster**} & \multicolumn{2}{c}{**Politics**} \\ \hline
**Label** & **Rank** & **Label** & **Rank** & **Label** & **Rank** & **Label** & **Rank** & **Label** & **Rank** \\ \hline IT\&Dig & 5 & **Military** & 3 & IT\&Dig & 8 & IT\&Dig & 10 & IT\&Dig & 6 \\ Invest & 12 & \
\begin{tabular}{c} Invest, \\ Finance \\ \end{tabular} & 6 & **Reading** & 14 &
\begin{tabular}{c} Invest \\ \end{tabular} & 11 & Finance & 8 \\
**Military** & 16 & **News** & 11 & **Fashion** & 19 & Finance & 15 & **News** & 9 \\ Finance & 17 & **Design** & 12 & **Education** & 20 & **Education** & 18 & **Military** & 11 \\
**Reading** & 20 & **History** & 15 & **Campus** & 21 & **Employment** & 19 & Invest & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Label frequency for the different rumor refuters. Figure 3: Male to female ratio in database. Additional implementations were applied to the variables to ensure the classification results were more valid and reliable:  
(1) The corresponding rumor refuting microblogs were deleted if they were one of the 11 most recent microblogs from the user. (2) A value of 0.5 was assigned to the microblogs/label information/verified information/signature sentiment or short text similarity analysis if the text was missing. (3) Words irrelevant to the content of the text but that significantly influenced the result of the sentiment analysis, such as \"Comment\", \"Like\", and \"Collect\", were removed. Step 2. The XGBoost model was utilized for refuter identification in the different categories. In this research, the XGBoost model got a bi-classification task. Thus, people who forwarded the anti-rumor microblog was treated as rumor refuters and labeled 1, people who only commented but not retweeted was treated as rumor stiflers and labeled 0. The samples were randomly divided into two parts, 80% for training the XGBoost model and the other 20% for testing the effect of the trained model. Two criteria; the F1 Score [33] and the AUC [34]; were applied for the classification result evaluation (as shown in Table 3). F1 score is a measure of a test's accuracy. It considers both the precision and the recall so that it is practical in classification tasks [33]. AUC is the probability of ranking the positive sample forward the negative sample whenever a positive and a negative samples are randomly selected. The higher the AUC, the better the classification result is [34]. With learning rate of 0.05, max_depth of 10, subsample value of 0.8, scale_pos_weight corresponding to the proportion of positive and negative samples, and keeping all the other parameters as default, the model is trained by Python Xgboost package (num_boost_round = 300 and early_stopping_rounds = 50). The efficiency of the XGBoost model can also be impacted by the number of samples.