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![EternalFC among the skeptic hubs. Phase diagrams that show the densities of Believers and Fact Checkers at equilibrium obtained by simulations of the model in a network of N=1000 nodes, formed by two communities of 500 agents associated with different values of α: the gullible (αgu=0.8) and the skeptic (αsk=0.3). The fixed parameters are β=0.5 and pf=0.1. Each cell (h,ρ) corresponds to the averaged value of the relative density over 20 simulations with h∗F0 eternal Fact Checkers (chosen among the highest degree nodes in the skeptic community) and ρ∗M rewiring trials, where h,ρ∈[0,1]](publication/337753314/figure/fig7/AS:963462201102350@1606718697799/EternalFC-among-the-skeptic-hubs-Phase-diagrams-that-show-the-densities-of-Believers-and.png)
EternalFC among the skeptic hubs. Phase diagrams that show the densities of Believers and Fact Checkers at equilibrium obtained by simulations of the model in a network of N=1000 nodes, formed by two communities of 500 agents associated with different values of α: the gullible (αgu=0.8) and the skeptic (αsk=0.3). The fixed parameters are β=0.5 and pf=0.1. Each cell (h,ρ) corresponds to the averaged value of the relative density over 20 simulations with h∗F0 eternal Fact Checkers (chosen among the highest degree nodes in the skeptic community) and ρ∗M rewiring trials, where h,ρ∈[0,1]
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Abstract We propose a framework to study the spreading of urban legends, i.e., false stories that become persistent in a local popular culture, where social groups are naturally segregated by virtue of many (both mutable and immutable) attributes. The goal of this work is identifying and testing new strategies to restrain the dissemination of false...
Citations
... [32] simulated the spreading of a hoax and its debunking at the same time taking forgetfulness into account by making a user lose interest in the fake news item with a given probability. The same authors extended their previous work comparing different fact-checking strategies on different network topologies to limit the spreading of fake news [31]. [22] studied the influence of online bots on a network through simulations, in an opinion dynamic setting. ...
To tackle the problem of disinformation, society must be aware not only of the existence of intentional misinformation campaigns, but also of the agents that introduce the misleading information, their supporting media, the nodes they use in social networks, the propaganda techniques they employ and their overall narratives and intentions. Disinformation is a challenge that must be addressed holistically: identifying and describing a disinformation campaign requires studying misinformation locally, at the message level, as well as globally, by modelling its propagation process to identify its sources and main players. In this paper, we argue that the integration of these two levels of analysis hinges on studying underlying features such as disinformation’s intentionality, and benefited and injured agents. Taking these features into account could make automated decisions more explainable for end users and analysts. Moreover, simultaneously identifying misleading messages, knowing their narratives and hidden intentions, modelling their diffusion in social networks, and monitoring the sources of disinformation will also allow a faster reaction, even anticipation, against the spreading of disinformation.
... According to the position with respect to the two opinions, and through interactions, individuals move their position, possibly influencing others: many elements can be employed in the model to drive the phenomenon, such as memory loss, the presence of leaders, the ability to convince others with a different opinion, varying levels of assertiveness, the fact that more extreme opinions are more difficult to change [20][21][22][23][24][25][26]. Compartmental modelling has also been used to study and understand the competition between fact-checkers and fake-news believers [27], how such dichotomy can be connected to network segregation [28], and-finally-how communication strategies can be tailored to make debunking more effective [29]. In this work, we focus on a peculiar instance of polarization, probably amplified by the uncontrolled diffusion of mis-and disinformation. ...
In the last years, vaccines debate has attracted the attention of all the social media, with an outstanding increase during COVID-19 vaccination campaigns. The topic has created at least two opposing factions, pro- and anti-vaccines, that have conflicting and incompatible narratives. However, a not negligible fraction of the population has an unclear position, as many citizens feel confused by the vast amount of information coming from both sides in the online social network. The engagement of the undecided population by the two parties has a key role in the success of the vaccination campaigns. In this article, we present three models used to describe the recruitment of the undecided population by pro-vax and no-vax factions in a three-states context. Starting from real-world data of Facebook pages previously labelled as pro-, anti-vaccines or neutral, we describe and compare three opinion dynamics models that catch different behaviours of the undecided population. The first one is a variation of the SIS model, where undecided position is considered an indifferent position, including users not interested in the discussion. Neutrals can be ‘infected’ by one of the two extreme factions, joining their side, and they ‘recover’ when they lose interest in the debate and go back to neutrality. The second model studied is a Voters model with three parties: neutral pages represent a centrist position. They lean on their original ideas, that are different from both the other parties. The last is the Bilingual model adapted to the vaccination debate: it describes a context where neutral individuals are in agreement with both pro- and anti-vax factions, with a position of compromise between the extremes (‘bilingualism’). If they have a one-sided neighbourhood, the necessity (or the convenience) to agree with both parties comes out, and bi-linguists can become mono-linguists. Our results depicts an agreement between the three models: anti-vax opinion propagates more than pro-vax, thanks to an initial strategic position in the online social network (even if they start with a smaller population). While most of the pro-vaccines nodes are segregated in their own communities, no-vaccines ones are entangled at the core of the network, where the majority of the undecided population is located. In the last section, we propose and compare some policies that could be applied to the network to prevent anti-vax overcome: they lead us to conclude that censoring strategies are not effective, as well as segregating scenarios based on unfollowing decisions, while the addition of links in the network favours the containment of the pro-vax domain, reducing the distance between pro-vaxxers and undecided population.
... 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). ...
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.
... 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. ...
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 (\textit{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 (\textit{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.
... Fact-checking, as one of the underlying mechanisms that shape the dynamics of the diffusion process, has been introduced with the hoax epidemic model 2 by Tambuscio et al. [195,193,194]. Also, other mechanisms such as a varying forgetting behaviour and different underlying network structures are ingredients of the model, to embed other cognitive and relational driven characteristics to the analytical framework, and to test different what-if scenarios. ...
... An agent in the hoax epidemic model can be in any of the following three states: 'Susceptible' (S), if they have not been exposed neither to the fake-news nor the fact checking, or if they have previously forgotten about it; 'Believer' (B), if they believe in the news and choose to spread it; and 'Fact-checker' (F) if they know the news is actually false -for example after having consulted a trusted debunking site -and choose to spread the fact-checking. The ith [195,193,194]. ...
... However, it is still possible to identify the most promising "immunisation" strategies to contain the spread of misinformation as much as possible. Several instances of the hoax epidemic model have been compared in the what-if analysis presented in [194]. First of all, to represent as much as possible a world where fake-news related to some conspiracy theories are spreading, the forgetting probability should be set to a very low value (e.g., p f = 0.1), and no believer can turn to a fact-checker directly (i.e., p v = 0). ...
The history of journalism and news diffusion is tightly coupled with the effort to dispel hoaxes, misinformation, propaganda, unverified rumours, poor reporting, and messages containing hate and divisions. With the explosive growth of online social media and billions of individuals engaged with consuming, creating, and sharing news, this ancient problem has surfaced with a renewed intensity threatening our democracies, public health, and news outlets credibility. This has triggered many researchers to develop new methods for studying, understanding, detecting, and preventing fake-news diffusion; as a consequence, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines to struggle in search of open problems and most relevant trends. The aim of this survey is threefold: first, we want to provide the researchers interested in this multidisciplinary and challenging area with a network-based analysis of the existing literature to assist them with a visual exploration of papers that can be of interest; second, we present a selection of the main results achieved so far adopting the network as an unifying framework to represent and make sense of data, to model diffusion processes, and to evaluate different debunking strategies. Finally, we present an outline of the most relevant research trends focusing on the moving target of fake-news, bots, and trolls identification by means of data mining and text technologies; despite scholars working on computational linguistics and networks traditionally belong to different scientific communities, we expect that forthcoming computational approaches to prevent fake news from polluting the social media must be developed using hybrid and up-to-date methodologies.
... Fact-checking efforts have been touted as among the most promising solutions to fight falseness and information with the intent to harm [78,77]. Thus, factchecking initiatives are recognized as an important endeavor to mitigate the effects of false information after the recognition that misinformation has a high potential to affect societies on a global scale [75]. ...
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 ramps 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, discerning whether there is a pattern of behavior through the countries. We rely on the open Twitter application programming interface (API) connection to gather data from public accounts of the six major fact-checking agencies in Latin America: Argentina (\textit{Chequeado}), Brazil (\textit{Agência Lupa}), Chile (\textit{Agência Lupa}), Colombia (\textit{Colombia Check} from \textit{Consejo de Redacción}), Mexico (\textit{El Sabueso} from \textit{Animal Político}), and Venezuela (\textit{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, evidencing opportunities to understand and slow the spread of false information.
... 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]. ...
... Same as [9]. [39] Tests fact-checking strategies on different network topologies. Same as [9]. ...
... 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. ...
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.
... As technology spawned the dilemma of fake news, it is tempting to assume that technology can solve it. In this context, scholars proposed a range of strategies to curb the dissemination of false information on social media, for instance, focusing on the role of network polarization [54]. In addition, artificial intelligence (AI) solutions have been particularly effective in detecting and removing dubious or undesirable content online. ...
The rise of social media has ignited an unprecedented circulation of false information in our society. It is even more evident in times of crisis, such as the COVID-19 pandemic. Fact-checking efforts have significantly expanded and have been touted as among the most promising solutions to fake news. Several studies have reported the development of fact-checking organizations in Western societies, albeit little attention has been given to the Global South. Here, to fill this gap, we introduce a novel Markov-inspired computational method for identifying topics in tweets. In contrast to other topic modeling approaches, our method clusters topics and their current evolution in a predefined time window. To conduct our experiments, we collected data from Twitter accounts of two Brazilian fact-checking outlets and presented the topics debunked by these initiatives in fortnights throughout the pandemic. By comparing these organizations, we could identify similarities and differences in what was shared by them. Our method resulted in an important technique to cluster topics in a wide range of scenarios, including an infodemic-a period overabundance of the same information. In particular, our results revealed a complex intertwining between politics and the health crisis during this period. We conclude by proposing a novel method which, in our opinion, is suitable for topic modeling and also an agenda for future research.
... As technology spawned the dilemma of fake news, it is tempting to assume that technology can solve it. In this context, scholars proposed a range of strategies to curb the dissemination of false information on social media, for instance, focusing on the role of network polarization [52]. In addition, artificial intelligence (AI) solutions have been particularly effective in detecting and removing dubious or undesirable content online. ...
The rise of social media has ignited an unprecedented circulation of false information in our society. It is even more evident in times of crises, such as the COVID-19 pandemic. Fact-checking efforts have expanded greatly and have been touted as among the most promising solutions to fake news, especially in times like these. Several studies have reported the development of fact-checking organizations in Western societies, albeit little attention has been given to the Global South. Here, to fill this gap, we introduce a novel Markov-inspired computational method for identifying topics in tweets. In contrast to other topic modeling approaches, our method clusters topics and their current evolution in a predefined time window. Through these, we collected data from Twitter accounts of two Brazilian fact-checking outlets and presented the topics debunked by these initiatives in fortnights throughout the pandemic. By comparing these organizations, we could identify similarities and differences in what was shared by them. Our method resulted in an important technique to cluster topics in a wide range of scenarios, including an infodemic -- a period overabundance of the same information. In particular, the data clearly revealed a complex intertwining between politics and the health crisis during this period. We conclude by proposing a generic model which, in our opinion, is suitable for topic modeling and an agenda for future research.
... In [15] the dynamics of the process has been explored extensively varying the credibility and verify probability that rule the victory of the hoax (high α low p v ) or the debunking (low α high p v ); moreover, it was found analytically a threshold for the verifying probability that assures the false news will be eradicated. In following papers other more complex versions of the model have been studied exploring the role of network segregation [30] and effective fact-checking strategies placing some never-forgetting debunkers in specific nodes of the network [31]. ...
... For instance, the above mentioned scenario converges to an amount of about 70% Fact-checker, 8% Believer and 22% Susceptible agents. The results obtained here and the ones described in a similar work on hoax diffusion [15], [31] are equivalent. By modifying network configurations as well as formula parameters, these convergence results constitute a verification of the proposed agent-based simulation. ...
