Combating Rumor Spread on Social Media: The Effectiveness of Refutation and Warning

Öztürk, Pınar; Li, Huaye; Sakamoto, Yasuaki

doi:10.2139/ssrn.2564249

Cited by 42 publications

(99 citation statements)

References 36 publications

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“…However, when the information is not corrected immediately, the episodic model generated while reading the misinformation is already integrated into the schematic representation of the issue. As noted in past research, “the initial integration of information when the model is being built is more readily performed than is its updating after a retraction” (Ozturk, Li, & Sakamoto, 2015, p. 2407).…”

Section: Discussionmentioning

confidence: 99%

A Meta-Analytic Examination of the Continued Influence of Misinformation in the Face of Correction: How Powerful Is It, Why Does It Happen, and How to Stop It?

Walter

Tukachinsky

2019

Communication Research

322

221

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A meta-analysis was conducted to examine the extent of continued influence of misinformation in the face of correction and the theoretical explanations of this phenomenon. Aggregation of results from 32 studies ( N = 6,527) revealed that, on average, correction does not entirely eliminate the effect of misinformation ( r = –.05, p = .045). Corrective messages were found to be more successful when they are coherent, consistent with the audience’s worldview, and delivered by the source of the misinformation itself. Corrections are less effective if the misinformation was attributed to a credible source, the misinformation has been repeated multiple times prior to correction, or when there was a time lag between the delivery of the misinformation and the correction. These findings are consistent with predictions based on theories of mental models and offer concrete recommendations for practitioners.

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Section: Discussionmentioning

confidence: 99%

A Meta-Analytic Examination of the Continued Influence of Misinformation in the Face of Correction: How Powerful Is It, Why Does It Happen, and How to Stop It?

Walter

Tukachinsky

2019

Communication Research

322

221

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“…For example, works such as Tanaka et al () examined if users could be deterred from spreading rumors by exposing them criticisms of the rumors. On a related note, works such as Ozturk et al () examined several interface design ideas that could help curb the spread of rumors.…”

Section: Resultsmentioning

confidence: 99%

“…First, even though the threats to the quality of UGC represent a growing concern as evident from the scholarly efforts from various disciplines, relatively little efforts have been spent in mitigating the threats. Among the few studies, some examined interface design strategies for reducing the spread of rumors by exposure to both rumors and their rebuttals (Chen, Sin, Theng, Lee, ; Ozturk et al, ). Others investigated the effect of source credibility on the effectiveness of rumor rebuttals (Bordia, DiFonzo, & Schulz, ).…”

Section: Discussionmentioning

confidence: 99%

Reviewing the landscape of research on the threats to the quality of user‐generated content

Pal

Chua

2016

Proc. Assoc. Info. Sci. Tech.

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The objective of the paper is to review and synthesize scholarly articles on the threats to the quality of usergenerated content (UGC). In this paper, threats to the quality of UGC are defined as the perpetration of misleading information caused by the lack of editorial control. They include deception, disinformation, manipulation, misinformation and rumors whose veracity cannot be easily established. In particular, this paper identifies (a) the research objectives that had been investigated, (b) the research methods that had been employed, and (c) the disciplines that studied the threats to the quality of UGC. The dominant research objective includes investigating the dynamics of threats. The most widely adopted research methods include quantitative analysis of real world data. This area of research was found to attract both intra-disciplinary and inter-disciplinary scholarly attention. It even attracted attention from practitioners affiliated to non-academic institutes. Finally, this paper serves as a call for scholars to identify possible ways to mitigate the threats to the quality of UGC. It also encourages the use of qualitative approaches.

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“…In this case, the task of predicting the popularity of a rumor micro-post based on the structure of the social network is not applicable. Also, in contrast to long-standing rumors, breaking news rumors have a distinctive life-cycle where a single verification statement from the authorities can quickly curb the diffusion of a breaking news rumor [Ozturk et al 2015;Takayasu et al 2015]. Thus, using early observations of a rumor's dynamics might not give a reliable estimation of its future popularity.…”

Section: Introductionmentioning

confidence: 99%

Detecting High-Engaging Breaking News Rumors in Social Media

Alkhodair

Fung

Ding

et al. 2020

ACM Trans. Manage. Inf. Syst.

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Users from all over the world increasingly adopt social media for newsgathering, especially during breaking news. Breaking news is an unexpected event that is currently developing. Early stages of breaking news are usually associated with lots of unverified information, i.e., rumors. Efficiently detecting and acting upon rumors in a timely fashion is of high importance to minimize their harmful effects. Yet, not all rumors have the potential to spread in social media. High-engaging rumors are those written in a manner that ensures achievement of the highest prevalence among the recipients. They are difficult to detect, spread very fast, and can cause serious damage to society. In this article, we propose a new multi-task Convolutional Neural Network (CNN) attention-based neural network architecture to jointly learn the two tasks of breaking news rumors detection and breaking news rumors popularity prediction in social media. The proposed model learns the salient semantic similarities among important features for detecting high-engaging breaking news rumors and separates them from the rest of the input text. Extensive experiments on five real-life datasets of breaking news suggest that our proposed model outperforms all baselines and is capable of detecting breaking news rumors and predicting their future popularity with high accuracy.

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Combating Rumor Spread on Social Media: The Effectiveness of Refutation and Warning

Cited by 42 publications

References 36 publications

A Meta-Analytic Examination of the Continued Influence of Misinformation in the Face of Correction: How Powerful Is It, Why Does It Happen, and How to Stop It?

A Meta-Analytic Examination of the Continued Influence of Misinformation in the Face of Correction: How Powerful Is It, Why Does It Happen, and How to Stop It?

Reviewing the landscape of research on the threats to the quality of user‐generated content

Detecting High-Engaging Breaking News Rumors in Social Media

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