| dc.description.abstract | Accelerated by the widespread use of social media, Internet rumors in major public health emergencies will destroy urban resilience. To find the path to improve the effectiveness of government rumor–refutation in major public health emergencies to enhance urban resilience, this paper creatively establishes an assessment research structure of the government’s rumor–refutation effectiveness in major public health emergencies, and an assessment criteria system from four perspectives of source, message, channel, and reviewer is constructed, an assessment method incorporating multicriteria decision-making and machine learning methods, i.e., optimal clustering-VIKORSort with hesitant fuzzy linguistic term sets based on combinatorial weighting is proposed. Subsequently, all 102 cases of government rumor–refutation during the COVID-19 lockdown in Wuhan in 2020 are taken as alternatives and assessed. The results show that Wuhan’s rumor–refutation effectiveness was not strong. Then, the investigated factors that constrain Wuhan’s emergency rumor–refutation effectiveness are diversified. Furthermore, this paper assesses the rumor–refutation effectiveness in Shanghai during the 2022 epidemic, obtains similar problems to Wuhan, and demonstrates the generalizability and robustness of the proposed method. Finally, based on the results, this paper proposes suggestions for improving the government’s rumor–refutation effectiveness in major public health emergencies to enhance urban resilience, which is a crucial contribution to combating Internet rumors and improving urban resilience in major public health emergencies. | |
