Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checks

Hunt, Kyle; Wang, Bairong; Zhuang, Jun

doi:10.1007/s11069-020-04016-6

Cited by 49 publications

(36 citation statements)

References 32 publications

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“…While our findings suggest that satire only requires a rebuttal if it is misinterpreted by many users, especially those rumors which are hard to verify by the individuals should be quickly rebutted by the police. The impact of government accounts in debunking could also be found in the study of Hunt et al (2020), who conducted a case study on the use of Twitter during two Hurricanes in the United States. The fact that the original debunking messages by the users mainly contain media content underlines that the media bear a great responsibility in debunking rumors, too.…”

Section: Discussionmentioning

confidence: 92%

“…Andrews et al (2016), who investigated the role of official accounts in the context of rumor propagation, showed that debunking messages posted by an official account motivate other users to correct earlier false information. According to Hunt et al (2020), most of the debunking tweets by individual users are related to news agencies and government organizations. However, the findings also showed that messages affirming the rumor received more attention than those debunking it.…”

Section: Debunking False Information On Social Mediamentioning

confidence: 99%

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

2020

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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.

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Section: Discussionmentioning

confidence: 92%

Section: Debunking False Information On Social Mediamentioning

confidence: 99%

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

2020

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“…However, verified users often have larger follower counts and may create greater total impressions, or tweet views, due to the size of their following (Wang & Zhuang, 2017). During crisis events, users interact most frequently with verified users (Hunt et al, 2020). Future research should examine specifically the relationship between verified users, tweet timing, and total impressions during surging misinformation events.…”

Section: Discussionmentioning

confidence: 99%

“…In our sample, only 1.7% of debunking tweets included a non-specific authority appeal. Twitter users frequently provide website links to debunk false rumors (Hunt et al, 2020). Debunking efforts should focus on including links and appeals to specific authority references.…”

Section: Discussionmentioning

confidence: 99%

Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths

2020

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This research examines the content, timing, and spread of COVID-19 misinformation and subsequent debunking efforts for two COVID-19 myths. COVID-19 misinformation tweets included more non-specific authority references (e.g., “Taiwanese experts”, “a doctor friend”), while debunking tweets included more specific and verifiable authority references (e.g., the CDC, the World Health Organization, Snopes). Findings illustrate a delayed debunking response to COVID-19 misinformation, as it took seven days for debunking tweets to match the quantity of misinformation tweets. The use of non-specific authority references in tweets was associated with decreased tweet engagement, suggesting the importance of citing specific sources when refuting health misinformation.

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“…At present, there are many researches on the adoption of information disclosure measures to reverse rumors in the process of online rumor propagation, focusing on the analysis of the effectiveness of the adoption of debunking rumor strategy, and the specific link of the adoption of user nodes can enhance the influence of debunking rumor, etc. However, there are relatively few strategic analyses on the intervention time for control strategy ( Hunt, Wang, & Zhuang, 2020 ). In order to strengthen the reversal effect of online rumors, the official institutions should enter the online rumor spreading system as soon as possible and implement mandatory measures to intervene in rumor spreading in a timely manner, such as single strategy or mixed control strategies, so as to reduce the scale of rumor spreading and contain rumor spreading in the initial stage.…”

Section: Discussionmentioning

confidence: 99%

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

Wang

et al. 2021

Information Processing & Management

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The development of the Internet and social media has expanded the speed and scope of information dissemination, but not all widely disseminated information is true. Especially during the public health emergencies, the endogenous health information demand generated by the lack of scientific knowledge of health information among online users stimulates the dissemination of health information by mass media while providing opportunities for rumor mongers to publish and spread online rumors. Invalid scientific knowledge and rumors will have a serious negative impact and disrupt social order during epidemic outbreaks such as COVID-19. Therefore, it is extremely important to construct an effective online rumor reversal model. The purpose of this study is to build an online rumor reversal model to control the spread of online rumors and reduce their negative impact. From the perspective of internal and external factors, based on the SIR model, this study constructed a G-SCNDR online rumor reversal model by adopting scientific knowledge level theory and an external online rumor control strategy. In this study, the G-SCNDR model is simulated, and a sensitivity analysis of the important parameters of the model is performed. The reversal efficiency of the G-SCNDR model can be improved by properly adopting the isolation-conversion strategy as the external control approach to online rumors with improving the popularization rate of the level of users' scientific knowledge and accelerating the transformation efficiency of official nodes. This study can help provide a better understanding of the process of online rumor spreading and reversing, as well as offering ceritain guidance and countermeasures for online rumor control during public health emergencies.

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Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checks

Cited by 49 publications

References 32 publications

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

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

Misinformation more likely to use non-specific authority references: Twitter analysis of two COVID-19 myths

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

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