DeVerna MR, Aiyappa R, Pacheco D, Bryden J, Menczer F

PLoS ONE 19(5): e0302201

The world’s digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content—so-called superspreaders—are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse.

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Daniel Rico and Yérali Gandica

Entropy 2024, 26(5), 363

Social media has dramatically influenced how individuals and groups express their demands, concerns, and aspirations during social demonstrations. The study of X or Twitter hashtags during those events has revealed the presence of some temporal points characterised by high correlation among their participants. It has also been reported that the connectivity presents a modular-to-nested transition at the point of maximum correlation. The present study aims to determine whether it is possible to characterise this transition using entropic-based tools. Our results show that entropic analysis can effectively find the transition point to the nested structure, allowing researchers to know that the transition occurs without the need for a network representation. The entropic analysis also shows that the modular-to-nested transition is characterised not by the diversity in the number of hashtags users post but by how many hashtags they share.

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Vivek Jadhav, Roberto Pasqua, Christophe Zanon, Matthieu Roy, Gilles Tredan, Richard Bon, Vishwesha Guttal, Guy Theraulaz

Across taxa, group-living organisms exhibit collective escape responses to stimuli varying from mild stress to predatory pressures. How exactly does information flow among group members leading to a collective escape remains an open question. Here we study the collective responses of a flock of sheep to a shepherd dog in a driving task between well-defined target points. We collected high-resolution spatio-temporal data from 14 sheep and the dog, using Ultra Wide Band tags attached to each individual. Through the time delay analysis of velocity correlations, we identify a hierarchy among sheep in terms of directional influence. Notably, the average spatial position of a sheep along the front-back axis of the group’s velocity strongly correlates with its impact on the collective movement. Our findings demonstrate that, counter-intuitively, directional information on shorter time scales propagates from the front of the group towards the rear, and that the dog exhibits adaptive movement adjustments in response to the flock’s dynamics. Furthermore, we show that a simple shepherding model can capture key features of the collective response of the sheep flocks. In conclusion, our study reveals novel insights on how directional information propagates in escaping animal groups.

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Andrea Musso and Dirk Helbing

Royal Society Open Science

May 2024 Volume 11Issue 5

Socio-diversity, the variety of human opinions, ideas, behaviours and styles, has profound implications for social systems. While it fuels innovation, productivity and collective intelligence, it can also complicate communication and erode trust. So what mechanisms can influence it? This paper studies how fundamental characteristics of social networks can support or hinder socio-diversity. It employs models of cultural evolution, mathematical analysis and numerical simulations. We find that pronounced inequalities in the distribution of connections obstruct socio-diversity. By contrast, the prevalence of close-knit communities, a scarcity of long-range connections, and a significant tie density tend to promote it. These results open new perspectives for understanding how to change social networks to sustain more socio-diversity and, thereby, societal innovation, collective intelligence and productivity.

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Rafael Prieto-Curiel, Ola Ali, Elma Dervić, Fariba Karimi, Elisa Omodei, Rainer Stütz, Georg Heiler, Yurij Holovatch

PNAS Nexus, Volume 3, Issue 5, May 2024, page 178,

Migration’s impact spans various social dimensions, including demography, sustainability, politics, economy, and gender disparities. Yet, the decision-making process behind migrants choosing their destination remains elusive. Existing models primarily rely on population size and travel distance to explain the spatial patterns of migration flows, overlooking significant population heterogeneities. Paradoxically, migrants often travel long distances and to smaller destinations if their diaspora is present in those locations. To address this gap, we propose the diaspora model of migration, incorporating intensity (the number of people moving to a country), and assortativity (the destination within the country). Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence. Despite its simplicity, our model accurately reproduces the observed stable flow and distribution of migration in Austria (postal code level) and US metropolitan areas, yielding precise estimates of migrant inflow at various geographic scales. Given the increase in international migrations, this study enlightens our understanding of migration flow heterogeneities, helping design more inclusive, integrated cities.

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