Bao Tran Truong, Xiaodan Lou, Alessandro Flammini, Filippo Menczer

PNAS Nexus, Volume 3, Issue 7, July 2024, pgae258,

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content. We find that the presence of hub accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.

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Marten Scheffer, W. Neil Adger, Stephen R. Carpenter, Carl Folke, Tim Lenton, Gaia Vince, Frances Westley, Chi Xu

One Earth VOLUME 7, ISSUE 7, P1151-1154, JULY 19, 2024

Climate change will worsen conditions for people in the Global South, while conditions in large parts of the North will improve. Migration seems an effective adaptation strategy. However, making that a win-win for migrants and receiving communities requires revision of the food system, rules for mobility, and strategies for social integration.

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Cristian Axenie, Oliver López-Corona, Michail A. Makridis, Meisam Akbarzadeh, Matteo Saveriano, Alexandru Stancu & Jeffrey West
npj Complexity volume 1, Article number: 12 (2024)

Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system’s output response to input variability. Systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems’ antifragility. We frame this review within three scales common to technical systems: intrinsic (input–output nonlinearity), inherited (extrinsic environmental signals), and induced (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility–adaptiveness–resilience–robustness–antifragility, the principles behind it, and its practical implications.

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Qian Zhang, Peyman Arebi
Journal of Complex Networks, Volume 12, Issue 4, August 2024, cnae027,

The main goal of controllability network methods on complex temporal networks is to control all nodes with the minimum number of control nodes. Real-world complex temporal networks are faced with many errors and attacks that cause the network structure to be changed in some way so that the controllability processes are disturbed and after that, the controllability robustness of the network decreases. One of the most important attacks on complex temporal networks is intelligent attacks. In this paper, the types of intelligent attacks and their destructive effects on the controllability of complex temporal networks have been investigated. In order to increase the controllability robustness of the network against intelligent attacks, a novel graph model and strategies have been proposed on complex dynamic graph by adding new control nodes or adding new links to the network so that the network is protected against intelligent attacks. The results of simulation and comparing them with conventional methods demonstrate that the proposed node addition strategy has performed better than other methods and the improvement rate in terms of execution time is 60%. On the other hand, the proposed immunization strategy by adding links has kept the network controllable with a smaller number of links (38%) and less execution time (52%) compared to other methods.

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