Caution: Rumors ahead—A case study on the debunking of false information on Twitter

Abstract

As false information may spread rapidly on social media, a profound understanding of how it can be debunked is required. This study offers empirical insights into the development of rumors after they are debunked, the various user groups who are involved in the process, and their network structures. As crisis situations are highly sensitive to the spread of rumors, Twitter posts from during the 2017 G20 summit are examined. Tweets regarding five rumors that were debunked during this event were manually coded into the following categories: rumor, debunking message, uncertainty about rumor, uncertainty about debunking message, and others. Our findings show that rumors which are debunked early and vehemently by official sources are the most likely to be stopped. When individuals participate in the process, they typically do so by sharing uncommented media content, as opposed to contributing user-generated content. Depending on the conditions in which a rumor arises, different network structures can be found. Since some rumors are easier for individuals to verify than others, our results have implications for the priorities of journalists and official sources.

A rumor reversal model of online health information during the Covid-19 epidemic

“…The official intervention action require sufficient evidence to support and carry out relevant investigations, which leads to the higher cost of its transmission than that of online rumors, and the time of intervention lags behind that of online rumors ( Zou and Tang, 2020 ). However, the lag time is the key factor affecting the dynamic conversion efficiency of online rumors ( Tian & Ding, 2019 ; Jung et al., 2020 ). Before the official take the control strategy, the denial node is regulated by the parameter of scientific knowledge level, and the users with scientific knowledge contact the rumor node into the denial node.…”

The Influence of the Debunker’s Identity and Emotional Expression on the Sharing Behavior of Debunking Information

“…We grouped the relevant microblogs into five categories in line with Jung et al’s (2020) classification. This included the following:…”

“…The spread of rumors, especially on social media, has had a serious impact on network order and social development ( Lazer et al, 2018 ; Allen et al, 2020 ). It can cause panic, lead to false accusations, and interfere with the work of emergency response agencies, posing a threat to public safety ( Jung et al, 2020 ). Particularly, since the outbreak of COVID-19, the spread of rumors has become more concerning than the prevention and treatment of the disease itself, causing significant negative consequences ( Kassam, 2020 ).…”

“…The amount of debunking information is always less than that passing on rumors. This means that the overall impact of debunking information is limited ( Jung et al, 2020 ).…”

“…Scholars have conducted empirical analyses of rumor development. The findings have suggested that rumors that are debunked by official sources are more likely to be arrested from continuing to spread ( Andrews et al, 2016 ; Jung et al, 2020 ). Regarding the relationship between information sources and sharing behavior, Kim and Dennis (2019) found that highlighting the source of an article affects the extent to which readers trust the article, which in turn influences users’ engagement with the same (e.g., read, like, comment, and share).…”