Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning

Online platforms face pressure to keep their communities civil and respectful. Thus, the bannings of problematic online communities from mainstream platforms like Reddit and Facebook are often met with enthusiastic public reactions. However, this policy can lead users to migrate to alternative fringe platforms with lower moderation standards and where antisocial behaviors like trolling and harassment are widely accepted. As users of these communities often remain \ca across mainstream and fringe platforms, antisocial behaviors may spill over onto the mainstream platform. We study this possible spillover by analyzing around $70,000$ users from three banned communities that migrated to fringe platforms: r/The\_Donald, r/GenderCritical, and r/Incels. Using a difference-in-differences design, we contrast \ca users with matched counterparts to estimate the causal effect of fringe platform participation on users' antisocial behavior on Reddit. Our results show that participating in the fringe communities increases users' toxicity on Reddit (as measured by Perspective API) and involvement with subreddits similar to the banned community -- which often also breach platform norms. The effect intensifies with time and exposure to the fringe platform. In short, we find evidence for a spillover of antisocial behavior from fringe platforms onto Reddit via co-participation.Comment: 18 pages, 4 figures, 2 tables, submitte

Understanding Online Migration Decisions Following the Banning of Radical Communities

The proliferation of radical online communities and their violent offshoots has sparked great societal concern. However, the current practice of banning such communities from mainstream platforms has unintended consequences: (I) the further radicalization of their members in fringe platforms where they migrate; and (ii) the spillover of harmful content from fringe back onto mainstream platforms. Here, in a large observational study on two banned subreddits, r/The\_Donald and r/fatpeoplehate, we examine how factors associated with the RECRO radicalization framework relate to users' migration decisions. Specifically, we quantify how these factors affect users' decisions to post on fringe platforms and, for those who do, whether they continue posting on the mainstream platform. Our results show that individual-level factors, those relating to the behavior of users, are associated with the decision to post on the fringe platform. Whereas social-level factors, users' connection with the radical community, only affect the propensity to be coactive on both platforms. Overall, our findings pave the way for evidence-based moderation policies, as the decisions to migrate and remain coactive amplify unintended consequences of community bans.Comment: 19 pages, 3 figures, 3 table

Adapting to Disruptions: Flexibility as a Pillar of Supply Chain Resilience

Supply chain disruptions cause shortages of raw material and products. To increase resilience, i.e., the ability to cope with shocks, substituting goods in established supply chains can become an effective alternative to creating new distribution links. We demonstrate its impact on supply deficits through a detailed analysis of the US opioid distribution system. Reconstructing 40 billion empirical distribution paths, our data-driven model allows a unique inspection of policies that increase the substitution flexibility. Our approach enables policymakers to quantify the trade-off between increasing flexibility, i.e., reduced supply deficits, and increasing complexity of the supply chain, which could make it more expensive to operate

OxDNA to study species interactions

Molecular ecology uses molecular genetic data to answer traditional ecological questions in biogeography and biodiversity among others. Several ecological principles, such as the niche hypothesis and the competitive exclusions, are based on the fact that species compete for resources. More in general, it is now recognized that species interactions play a crucial role in determining the coexistence and abundance of species. However, experimentally controllable platforms, which allow to study and measure competitions among species, are rare and difficult to implement. In this work, we suggest to exploit a Molecular Dynamics coarse-grained model to study interactions among single strands of DNA, representing individuals of different species, which compete for binding to other oligomers considered as resources. In particular, the well-established knowledge of DNA-DNA interactions at the nanoscale allows us to test the hypothesis that the maximum consecutive overlap between pairs of oligomers measure the species competitive advantages. However, we suggest that more complex structure also plays a role in the ability of the species to successfully bind to the target resource oligomer. We complement the simulations with experiments on populations of DNA strands which qualitatively confirm our hypotheses. These tools constitute a promising starting point for further developments concerning the study of controlled, DNA-based, artificial ecosystems

Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding

We introduce a set of algorithms (Het-node2vec) that extend the original node2vec node-neighborhood sampling method to heterogeneous multigraphs, i.e. networks characterized by multiple types of nodes and edges. The resulting random walk samples capture both the structural characteristics of the graph and the semantics of the different types of nodes and edges. The proposed algorithms can focus their attention on specific node or edge types, allowing accurate representations also for underrepresented types of nodes/edges that are of interest for the prediction problem under investigation. These rich and well-focused representations can boost unsupervised and supervised learning on heterogeneous graphs.Comment: 20 pages, 5 figure

Lung ultrasound features and relationships with respiratory mechanics of evolving BPD in preterm rabbits and human neonates

Evolving bronchopulmonary dysplasia (BPD) is characterized by impaired alveolarization leading to lung aeration inhomogeneities. Hyperoxia-exposed preterm rabbits have been proposed to mimic evolving BPD; therefore, we aimed to verify if this model has the same lung ultrasound and mechanical features of evolving BPD in human neonates. Semiquantitative lung ultrasound and lung mechanics measurement was performed in 25 preterm rabbits (28days of gestation) and 25 neonates (mean gestational age approximate to 26wk) with evolving BPD. A modified rabbit lung ultrasound score (rLUS) and a validated neonatal lung ultrasound score (WS) were used. Lung ultrasound images were recorded and evaluated by two independent observers blinded to each other's evaluation. Lung ultrasound findings were equally heterogeneous both in rabbits as in human neonates and encompassed all the classical lung ultrasound semiology. Lung ultrasound and histology examination were also performed in 13 term rabbits kept under normoxia as further control and showed the absence of ultrasound and histology abnormalities compared with hyperoxia-exposed preterm rabbits. The interrater absolute agreement for the evaluation of lung ultrasound images in rabbits was very high [ICC: 0.989 (95%Cl: 0.975-0.995); P < 0.0001], and there was no difference between the two observers. Lung mechanics parameters were similarly altered in both rabbits and human neonates. There were moderately significant correlations between airway resistances and lung ultrasound scores in rabbits (rho = 0.519; P = 0.008) and in neonates (rho = 0.409; P = 0.042). In conclusion, the preterm rabbit model fairly reproduces the lung ultrasound and mechanical characteristics of preterm neonates with evolving BPD.NEW & NOTEWORTHY We have reported that hyperoxia-exposed preterm rabbits and human preterm neonates with evolving BPD have the same lung ultrasound appearance, and that lung ultrasound can be fruitfully applied on this model with a brief training. The animal model and human neonates also presented the same relationship between semiquantitative ultrasound-assessed lung aeration and airway resistances. In conclusion, this animal model fairly reproduce evolving BPD as it is seen in clinical practice

Image-Guided Intraoperative Assessment of Surgical Margins in Oral Cavity Squamous Cell Cancer: A Diagnostic Test Accuracy Review

(1) Background: The assessment of resection margins during surgery of oral cavity squamous cell cancer (OCSCC) dramatically impacts the prognosis of the patient as well as the need for adjuvant treatment in the future. Currently there is an unmet need to improve OCSCC surgical margins which appear to be involved in around 45% cases. Intraoperative imaging techniques, magnetic resonance imaging (MRI) and intraoral ultrasound (ioUS), have emerged as promising tools in guiding surgical resection, although the number of studies available on this subject is still low. The aim of this diagnostic test accuracy (DTA) review is to investigate the accuracy of intraoperative imaging in the assessment of OCSCC margins. (2) Methods: By using the Cochrane-supported platform Review Manager version 5.4, a systematic search was performed on the online databases MEDLINE-EMBASE-CENTRAL using the keywords "oral cavity cancer, squamous cell carcinoma, tongue cancer, surgical margins, magnetic resonance imaging, intraoperative, intra-oral ultrasound". (3) Results: Ten papers were identified for full-text analysis. The negative predictive value (cutoff < 5 mm) for ioUS ranged from 0.55 to 0.91, that of MRI ranged from 0.5 to 0.91; accuracy analysis performed on four selected studies showed a sensitivity ranging from 0.07 to 0.75 and specificity ranging from 0.81 to 1. Image guidance allowed for a mean improvement in free margin resection of 35%. (4) Conclusions: IoUS shows comparable accuracy to that of ex vivo MRI for the assessment of close and involved surgical margins, and should be preferred as the more affordable and reproducible technique. Both techniques showed higher diagnostic yield if applied to early OCSCC (T1-T2 stages), and when histology is favorable

Nanoscale insight into the exfoliation mechanism of graphene with organic dyes: effect of charge, dipole and molecular structure

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effect of charge, dipole and molecular structure

We study the mechanism of surface adsorption of organic dyes on graphene, and successive exfoliation in water of these dye-functionalized graphene sheets. A systematic, comparative study is performed on pyrenes functionalized with an increasing number of sulfonic groups. By combining experimental and modeling investigations, we find an unambiguous correlation between the graphene–dye interaction energy, the molecular structure and the amount of graphene flakes solubilized. The results obtained indicate that the molecular dipole is not important per se, but because it facilitates adsorption on graphene by a “sliding” mechanism of the molecule into the solvent layer, facilitating the lateral displacement of the water molecules collocated between the aromatic cores of the dye and graphene. While a large dipole and molecular asymmetry promote the adsorption of the molecule on graphene, the stability and pH response of the suspensions obtained depend on colloidal stabilization, with no significant influence of molecular charging and dipole

GraPE: fast and scalable Graph Processing and Embedding

Graph Representation Learning methods have enabled a wide range of learning problems to be addressed for data that can be represented in graph form. Nevertheless, several real world problems in economy, biology, medicine and other fields raised relevant scaling problems with existing methods and their software implementation, due to the size of real world graphs characterized by millions of nodes and billions of edges. We present GraPE, a software resource for graph processing and random walk based embedding, that can scale with large and high-degree graphs and significantly speed up-computation. GraPE comprises specialized data structures, algorithms, and a fast parallel implementation that displays everal orders of magnitude improvement in empirical space and time complexity compared to state of the art software resources, with a corresponding boost in the performance of machine learning methods for edge and node label prediction and for the unsupervised analysis of graphs.GraPE is designed to run on laptop and desktop computers, as well as on high performance computing cluster