Reconstructing Mesoscale Network Structures

Jeude, Jeroen van Lidth de; Clemente, Riccardo Di; Caldarelli, Guido; Saracco, Fabio; Squartini, Tiziano

doi:10.1155/2019/5120581

Cited by 18 publications

(28 citation statements)

References 34 publications

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“…several African nations, throughout our dataset). Notice that core size increases with time: apparently, thus, the system becomes increasingly integrated, confirming a result found in [17], where it was shown that the size of the WTW strongly connected component increases with time as well. a more refined criterion to judge the goodness of a fit is employed: solutions like the one of adopting criteria like the Akaike Information Criterion et similia have been proposed [29].…”

Section: Discussionsupporting

confidence: 85%

Detecting core-periphery structures by surprise

Jeude¹,

Caldarelli²,

Squartini³

2019

EPL

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Detecting the presence of mesoscale structures in complex networks is of primary importance. This is especially true for financial networks, whose structural organization deeply affects their resilience to events like default cascades, shocks propagation, etc. Several methods have been proposed, so far, to detect communities, i.e. groups of nodes whose internal connectivity is significantly large. Communities, however do not represent the only kind of mesoscale structures characterizing realworld networks: other examples are provided by bow-tie structures, core-periphery structures and bipartite structures. Here we propose a novel method to detect statistically-significant bimodular structures, i.e. either bipartite or core-periphery ones. It is based on a modification of the surprise, recently proposed for detecting communities. Our variant allows for bimodular nodes partitions to be revealed, by letting links to be placed either 1) within the core part and between the core and the periphery parts or 2) between the layers of a bipartite network. From a technical point of view, this is achieved by employing a multinomial hypergeometric distribution instead of the traditional, binomial hypergeometric one; as in the latter case, this allows a p-value to be assigned to any given (bi)partition of the nodes. To illustrate the performance of our method, we report the results of its application to several real-world networks, including social, economic and financial ones.

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Section: Discussionsupporting

confidence: 85%

Detecting core-periphery structures by surprise

Jeude¹,

Caldarelli²,

Squartini³

2019

EPL

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“…An arrow in the opposite direction means that u is retweeting t . To filter out the random noise from this network, we make use of the directed version of the BiCM, i.e., the Bipartite Directed Configuration Model ( BiDCM [ 46 ]), described in Sect. 6.2 .…”

Section: Resultsmentioning

confidence: 99%

Flow of online misinformation during the peak of the COVID-19 pandemic in Italy

et al. 2021

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The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network.We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community.

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“…In the previous section, we obtained the political affiliation of verified users by projecting the information in the bipartite network describing the interactions between verified and unverified users. Now, we will apply the entropy-based null model, in its directed variant-Bipartite Directed Configuration Model (BiDCM)-proposed by van Lidth de Jeude et al 48 , to filter the total exchange of content in our dataset, after discounting the information regarding the activity of users and the virality of messages, as in ref. 32 .…”

Section: Resultsmentioning

confidence: 99%

“…approach is similar to the one adopted for the extraction of the users political affiliation. The difference consists in (1) substituting the BiCM with its analogous directed version, the BiDCM (proposed by van Lidth de Jeude et al 48 ) and in (2) considering layers of different kind. While in the previous section layers represent verified and unverified users, here they represent tweets on one layer and users (both verified and unverified) on the other.…”

Section: Methodsmentioning

confidence: 99%

The role of bot squads in the political propaganda on Twitter

et al. 2020

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Nowadays, Social Media are a privileged channel for news spreading, information exchange, and fact checking. Unexpectedly for many users, automated accounts, known as social bots, contribute more and more to this process of information diffusion. Using Twitter as a benchmark, we consider the traffic exchanged, over one month of observation, on the migration flux from Northern Africa to Italy. We measure the significant traffic of tweets only, by implementing an entropy-based null model that discounts the activity of users and the virality of tweets. Results show that social bots play a central role in the exchange of significant content. Indeed, not only the strongest hubs have a number of bots among their followers higher than expected, but furthermore a group of them, that can be assigned to the same political tendency, share a common set of bots as followers. The retweeting activity of such automated accounts amplifies the hubs' messages.

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Reconstructing Mesoscale Network Structures

Cited by 18 publications

References 34 publications

Detecting core-periphery structures by surprise

Detecting core-periphery structures by surprise

Flow of online misinformation during the peak of the COVID-19 pandemic in Italy

The role of bot squads in the political propaganda on Twitter

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