Fact-checking strategies to limit urban legends spreading in a segregated society

Tambuscio, Marcella; Ruffo, Giancarlo

doi:10.1007/s41109-019-0233-1

Cited by 16 publications

(8 citation statements)

References 49 publications

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“…Fact-checking efforts have been touted among the most promising solutions to fight falseness and information with the intent to harm (Tandoc 2019;Tambuscio and Ruffo 2019). In fact, fact-checking became an important endeavor to mitigate the effects of false information after the recognition that misinformation has a high potential to impact society (Tambuscio et al 2018).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 fake news diffusion across Latin America

Ceron

Sanseverino

de-Lima-Santos

et al. 2021

Soc. Netw. Anal. Min.

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Fact-checking verifies a multitude of claims and remains a promising solution to fight fake news. The spread of rumors, hoaxes, and conspiracy theories online is evident in times of crisis, when fake news ramped up across platforms, increasing fear and confusion among the population as seen in the COVID-19 pandemic. This article explores fact-checking initiatives in Latin America, using an original Markov-based computational method to cluster topics on tweets and identify their diffusion between different datasets. Drawing on a mixture of quantitative and qualitative methods, including time-series analysis, network analysis and in-depth close reading, our article proposes an in-depth tracing of COVID-related false information across the region, comparing if there is a pattern of behavior through the countries. We rely on the open Twitter application programming interface connection to gather data from public accounts of the six major fact-checking agencies in Latin America, namely Argentina (Chequeado), Brazil (Agência Lupa), Chile (Mala Espina Check), Colombia (Colombia Check from Consejo de Redacciín), Mexico (El Sabueso from Animal Polótico) and Venezuela (Efecto Cocuyo). In total, these profiles account for 102,379 tweets that were collected between January and July 2020. Our study offers insights into the dynamics of online information dissemination beyond the national level and demonstrates how politics intertwine with the health crisis in this period. Our method is capable of clustering topics in a period of overabundance of information, as we fight not only a pandemic but also an infodemic, evidentiating opportunities to understand and slow the spread of false information.

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Section: Introductionmentioning

confidence: 99%

COVID-19 fake news diffusion across Latin America

Ceron

Sanseverino

de-Lima-Santos

et al. 2021

Soc. Netw. Anal. Min.

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“…Otherwise, the effect is not apparent. However, in some cases, such as urban legends, the fact-checker mechanism does not seem to be effective [173]. Young et al [218] verified video is more effective in rectifying public misperceptions.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Li,

Xia,

Wang

et al. 2021

Preprint

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Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing.) Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.CCS Concepts: • Human-centered computing → Social networks.

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“…They studied the existence of thresholds for the fact-checking probability that guarantees the complete removal of the fake news from the network and proved that such a threshold does not depend on the spreading rate, but only on the gullibility and forgetting probability of the users. The same authors extended their previous study assessing the role of network segregation in misinformation spreading [38] and comparing different fact-checking strategies on different network topologies to limit the spreading of fake news [39].…”

Section: Epidemic Modelingmentioning

confidence: 95%

“…Agents of type common and influencer can enter any of the possible states: susceptible, believer, and fact-checker. However, we further make our simulation more realistic by considering the presence of special influencers called eternal fact-checkers [39]. These influencers constantly participate in the debunking of any fake news item.…”

Section: Agent Modelingmentioning

confidence: 99%

Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

2021

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The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

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Fact-checking strategies to limit urban legends spreading in a segregated society

Cited by 16 publications

References 49 publications

COVID-19 fake news diffusion across Latin America

COVID-19 fake news diffusion across Latin America

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

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