Search

The article discusses research on potentially malicious social bots that use artificial intelligence on social media networks. Topics include the use of bots to influence political opinion on the online messaging service Twitter, the creation of systems to detect social bots, and the use of crowdsourcing to find social bots.

While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as “echo chambers.” Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both their opinions and social connections based on the information to which they are exposed through sharing. The model dynamics show that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.

Bioeconomy has been proposed as a pathway to sustainable development in many countries. However, the difficulties in defining the bioeconomy boundaries at the national level might necessitate the adoption of a sectoral approach to monitor and evaluate the success of its development. In this resolution, standards, certifications and labelling (SCL) schemes for bioeconomy-related sectors might be an essential source of data. The study evaluates the potential to use SCL schemes as a source for monitoring and evaluating sustainable bioeconomy, by analysing the sustainability aspects (chain-of-custody, environmental, economic and social themes) considered in selected SCL schemes. A variety of SCL schemes for different stages of the bioeconomy value chains were subjected to analysis on whether they consider internationally agreed aspirational principles and criteria for sustainable bioeconomy. The aspects most frequently mentioned by the requirement lists of the analysed SCL schemes were identified, along with the highlights on the least-frequently mentioned sustainability topics.

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here, we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to “forget” old memes and replace them over time with the new ones. The evaluation of our framework is carried out using a dataset of Twitter trending topics. Over a 1-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.

We present the results of analytical calculations and numerical simulations of the behavior of a new class of chain molecules that we call thick polymers. The concept of the thickness of such a polymer, viewed as a tube, is encapsulated by a special three‐body interaction and impacts on the behavior both locally and nonlocally. When thick polymers undergo compaction because of an attractive self‐interaction, we find a new type of phase transition between a compact phase and a swollen phase at zero temperature with increasing thickness. In the vicinity of this transition, short tubes form space‐filling helices and sheets as observed in protein native‐state structures. With increasing chain length, or with an increasing number of chains, we numerically find a crossover from secondary‐structure motifs to a quite distinct class of structures akin to the semicrystalline phases of polymers or amyloid fibers in polypeptides. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 650–679, 2005

The configuration space available to randomly cyclized polymers is divided into subspaces accessible to individual knot types. A phantom chain utilized in numerical simulations of polymers can explore all subspaces, whereas a real closed chain forming a figure-of-eight knot, for example, is confined to a subspace corresponding to this knot type only. One can conceptually compare the assembly of configuration spaces of various knot types to a complex foam where individual cells delimit the configuration space available to a given knot type. Neighboring cells in the foam harbor knots that can be converted into each other by just one intersegmental passage. Such a segment-segment passage occurring at the level of knotted configurations corresponds to a passage through the interface between neighboring cells in the foamy knot space. Using a DNA topoisomerase-inspired simulation approach we characterize here the effective interface area between neighboring knot spaces as well as the surface–to-volume ratio of individual knot spaces. These results provide a reference system required for better understanding mechanisms of action of various DNA topoisomerases.

A nonlinear model is studied which describes the evolution of a landscape under the effects of erosion and regeneration by geologic uplift by mean of a simple differential equation. The equation, already in wide use among geomorphologists and in that context obtained phenomenologically, is here derived by reparametrization invariance arguments and exactly solved in dimension d=1. Results of numerical simulations in d=2 show that the model is able to reproduce the critical scaling characterizing landscapes associated with natural river basins. We show that configurations minimizing the rate of energy dissipation (optimal channel networks) are stationary solutions of the equation describing the landscape evolution. Numerical simulations show that a careful annealing of the equation in the presence of additive noise leads to configurations very close to the global minimum of the dissipated energy, characterized by mean field exponents. We further show that if one considers generalized river network configurations in which splitting of the flow (i.e., braiding) and loops are allowed, the minimization of the dissipated energy results in spanning loopless configurations, under the constraints imposed by the continuity equations. This is stated in the form of a general theorem applicable to generic networks, suggesting that other branching structures occurring in nature may possibly arise as optimal structures minimizing a cost function.

Climate system teleconnections are crucial for improving climate predictability, but difficult to quantify. Standard approaches to identify teleconnections are often based on correlations between time series. Here we present a novel method leveraging Granger causality, which can infer/detect relationships between any two fields. We compare teleconnections identified by correlation and Granger causality at different timescales. We find that both Granger causality and correlation consistently recover known seasonal precipitation responses to the sea surface temperature pattern associated with the El Niño Southern Oscillation. Such findings are robust across multiple time resolutions. In addition, we identify candidates for unexplored teleconnection responses. Teleconnections, or climate responses far away from a perturbation, are difficult to study. We have developed a new method of quantifying teleconnection strengths and identifying new teleconnections. When testing this method on global precipitation responses to El Niño surface temperature changes, not only does our method do a good job of recovering previous findings, but we identify several new possible teleconnections. Our method can be applied to any two climate variables and could be useful for attribution studies, for example changes in frequency of extreme events. We propose a new method for identifying significant teleconnections between any two climate fieldsWe test this method on Niño 3.4 temperature teleconnections to precipitation anomalies everywhereThe method recovers previously known El Niño teleconnections and discovers potential new ones We propose a new method for identifying significant teleconnections between any two climate fields We test this method on Niño 3.4 temperature teleconnections to precipitation anomalies everywhere The method recovers previously known El Niño teleconnections and discovers potential new ones