## 5 Conclusions and Future Work  
The purpose of the current study was to determine the association between user features (including sentiment tendency, recent interests, gender, geographic distributions, age structure, and label frequency) and their refuting behaviors and so as to identify the rumor refuters, and deal with the dark side of online information behavior by accelerating the rumor refutation information dissemination. The findings shown in Table 7 reveal some general features of refuters as well as variations between refuters considering different rumor categories: (1) there were more male refuters than females, especially in the politics and military categories; (2) rumor refuters of all categories were found to be highly concentrated in East, North and South of China, and particularly in provinces with first-tier cities; (3) when users were from the same geographic locations as the refutation microblogs, they were less inclined to retweet economic, societal and disaster related rumor refutation microblogs; (4) refuters were mainly aged between 18 and 40, with the refuters in the politics and military categories being somewhat older than those in the economic, society, and disaster-related categories; and (5) the political and society related rumor refuters tended to follow and post relevant information more frequently, which was shown by their higher \(\overline{\text{MSm}}\). On the other hand, as it is shown in Table 8, there were significant differences between refuters and stiflers: (1) rumor refuters were found to be more active with higher NOM and NOF; (2) the ML was comparatively higher in the economic, political and societal categories; (3) in general, the refuters' VISm and LSm were significantly lower than the stiflers (except for the LSm in the societal category), but their SSm was higher; however, the refuters in the political category were found to have higher VISm and LSm than the stiflers and there were no significant differences between the SSm of rumor refuters and stiflers; (4) economic related rumor refuters had less positive microblog content sentiment, but the refuters tended to have higher \(\overline{\text{MSm}}\) in all categories. Provided that there was an adequately large amount of data, the XGBoost model was broadly applicable in identifying the refuters, regardless of differences in the rumor categories. This paper only takes the anti-rumor posts verified, confirmed, and announced by official accounts into consideration, but there is still a small chance that a rumor refuted by the platform turn out to be true eventually. In the future, we plan to examine the refuter characteristics in a wider range of microblog samples, hopefully covering the possible bias with large data. In addition, we will consider more personalized and individualized features beyond just demographic attributes to more precisely identify the refuter crowd. Analysis on influence and power of refuters is also a focus of future research. **Author Contributions:** Conceptualization, S.W., Z.L., and Y.W. ; Data Curation, S.W., Q.Z., and Y.W. ; Formal Analysis, S.W., Z.L., Y.W., and Q.Z. ; Investigation, S.W. and Y.W. ; Methodology, S.W., Z.L., Q.Z., and Y.W. ; Supervision, Z.L. ; Validation, S.W., Z.L., Y.W., and Q.Z. ; Writing--Original Draft, S.W., Z.L., Y.W., and Q.Z. ; Funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the China Postdoctoral Science Foundation Funded Project (Grant No. 2017M612983), Chengdu Philosophy and Social Science Planning Project (Grant No. 2019L40), and the Fundamental Research Funds for the Central Universities (Grant No. SCUBS-PY-202017). **Acknowledgments:** We thank the editors and any reviewers for their helpful comments. **Conflicts of Interest:** The authors declare no conflict of interest.