![]() The results reveal a linear relationship between the negative and neutral attitudes of netizens on social networking platforms. It selects group posts regarding the COVID-19 vaccination dispute on the Douban platform, analyzes the positions of different users, and explores phenomena related to users obtaining health information on domestic social platforms according to different topics and information behaviors. This study aims to explore phenomena and laws that occur when different users on social network platforms obtain health information by constructing an opinion mining model, analyzing the user's position on selected cases, and exploring the reflection of the phenomenon of truth decay on platforms. These results provide practical suggestions on misinformation correction on social media, and a tool to guide practitioners to revise corrections before publishing, leading to ideal efficacies. Finally, we build a regression model to predict correction effectiveness. For example, mentioning excessive original misinformation in corrections would not undermine people’s believability within a short period after reading warnings of misinformation in a demanding tone make correction worse determinants of correction effectiveness vary among different topics of misinformation. We demonstrate several promising conclusions through a comprehensive analysis of the dataset. Then, we obtain 1487 significant COVID-19 related corrections on Microblog between January 1st, 2020 and April 30th, 2020, and conduct annotations, which characterize each piece of correction based on the aforementioned factors. Specifically, referring to the Backfire Effect, Source Credibility, and Audience’s role in dissemination theories, we propose five hypotheses containing seven potential factors (regarding correction content and publishers’ influence), e.g., the proportion of original misinformation and warnings of misinformation. This study explores determinants governing the effectiveness of misinformation corrections on social media with a combination of a data-driven approach and related theories on psychology and communication. Previous works focus on psychological theories and experimental studies, while the applicability of conclusions to the actual social media is unclear. However, the effective mechanism of correction on social media is not fully verified. Correction is a crucial countermeasure to debunk misperceptions. The rapid dissemination of misinformation in social media during the COVID-19 pandemic triggers panic and threatens the pandemic preparedness and control. Experimental results demonstrate that the proposed method can identify critical edges more accurately in comparison to other source-ignorant methods. In this paper, a new source-ignorant method is proposed to identify a set of critical edges by considering for each edge the impact of blocking and the influence of the nodes connected to the edge. Taking into account additional features of edges (beyond centrality) may help determine what edges to block more accurately. Several source-ignorant edge blocking methods have been proposed which mostly determine critical edges on the basis of centrality. ![]() Although the detection of the sources of rumour may help identify critical edges this has an overhead that source-ignorant approaches are trying to eliminate. Blocking so-called critical edges, that is, edges that have a significant role in the spreading process, has attracted lots of attention as a means to minimize the spread of rumours. The spread of rumours in social networks has become a significant challenge in recent years.
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