Phase transitions in information spreading on structured populations

Davis, Jessica T.; Perra, Nicola; Zhang, Qian; Moreno, Yamir; Vespignani, Alessandro

doi:10.1038/s41567-020-0810-3

Cited by 75 publications

(42 citation statements)

References 45 publications

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“…Information spreading. Efforts to simulate the spreading of information on social media by reproducing real data have mostly applied variants of standard epidemic models [37][38][39][40] . Coherently, we analyze the observed monotonic increasing trend in the way new users interact with information related to the COVID-19 by using epidemic models.…”

Section: Interaction Patternsmentioning

confidence: 99%

The COVID-19 social media infodemic

Cinelli

Quattrociocchi

Galeazzi

et al. 2020

Sci Rep

1,671

1,101

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We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number $$R_0$$ R 0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors’ amplification.

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Section: Interaction Patternsmentioning

confidence: 99%

The COVID-19 social media infodemic

Cinelli

Quattrociocchi

Galeazzi

et al. 2020

Sci Rep

1,671

1,101

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show abstract

“…In addition, models of complex recovery, in which the social influence mechanism acts on the recovery rule rather than on the infection one, showed that this change of perspective might lead to explosive adoption dynamics [58]. This behavior is especially pronounced in spatial systems, whose effects on the contagion dynamics have also been the focus of several studies [59][60][61][62]. Yet, among humans, communication interactions can occur in groups of three or more agents, and often cannot be simply factored into a collection of dyadic contacts.…”

Section: Modeling Social Contagionmentioning

confidence: 99%

On the Dual Nature of Adoption Processes in Complex Networks

Iacopini

Latora

2021

Front. Phys.

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Adoption processes in socio-technological systems have been widely studied both empirically and theoretically. The way in which social norms, behaviors, and even items such as books, music, or other commercial or technological products spread in a population is usually modeled as a process of social contagion, in which the agents of a social system can infect their neighbors on the underlying network of social contacts. More recently, various models have also been proposed to reproduce the typical dynamics of a process of discovery, in which an agent explores a space of relations between ideas or items in search for novelties. In both types of processes, the structure of the underlying networks, respectively, the network of social contacts in the first case, and the network of relations among items in the second one, plays a fundamental role. However, the two processes have been traditionally seen and studied independently. Here, we provide a brief overview of the existing models of social spreading and exploration and of the latest advancements in both directions. We propose to look at them as two complementary aspects of the same adoption process: on the one hand, there are items spreading over a social network of individuals influencing each other, and on the other hand, individuals explore a network of similarities among items to adopt. The two-fold nature of the approach proposed opens up new stimulating challenges for the scientific community of network and data scientists. We conclude by outlining some possible directions that we believe may be relevant to be explored in the coming years.

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“…Unfortunately, with the spread of COVID-19 epidemic, another massive infodemic spread virally over the world with recurring drama. Previous evidence suggests that the Internet, by its very nature, could amplify and relay such infodemic swiftly worldwide, cause exaggerated panic and worsen stigmatization against people in the epicenter of outbreak progressively [16][17][18] . Much worse in the current infodemic, Corona beer is being slashed by the name's similarity to the deadly coronavirus.…”

Section: Does a Virus' Name Really Matter?mentioning

confidence: 99%

Infodemiological Study on COVID-19 Epidemic and COVID-19 Infodemic

Hu¹,

Yang²,

Li³

et al. 2020

Preprint

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Less aligned emphasis has been given to the COVID-19 infodemic coordinating with the COVID-19 outbreak. Global profusion of tangled monikers and hashtags has found their ways in daily communication and contributed to backlash against Chinese. Official naming efforts against infodemic should be meet with a fair share of identification. Based on brief critical reviews on previous multifarious naming practices, we punctuate heuristic introspection in scientific conventions and sociocultural paradigms. Infodemiological analysis promises to articulate that people around the globe are divided in their favor stigmatized monikers in the public and scientific communities because of perceptual bias. There is no positive correlation between the degree of infection in their territories and collective perceptual bias to COVID-19. The official portfolio “COVID-19” and “SARS-CoV-2” has not become de facto standard usages, but full-fledged official names are excepted to duly contribute to the resilience of negative perceptual bias and collective behavioral propensities.

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Phase transitions in information spreading on structured populations

Cited by 75 publications

References 45 publications

The COVID-19 social media infodemic

The COVID-19 social media infodemic

On the Dual Nature of Adoption Processes in Complex Networks

Infodemiological Study on COVID-19 Epidemic and COVID-19 Infodemic

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