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Cold chains are essential components of agrifood system activities, from storage and food processing to transport, retail, and household consumption. We devise a comprehensive methodology, validated against established data, to estimate both direct (due to refrigerant use) and indirect (from energy use) greenhouse gas emissions from agrifood system cold chains, expanding previous approaches, which had focused on direct emissions only. We found that in 2022, world-total indirect emissions from energy use in cold chains were more than twice the direct component from refrigerants. This resulted in a new estimate of world-total GHG emissions from agrifood system cold chains of 1.32 Gt CO2eq in 2022, with significant growth over the past two decades (0.52 Gt CO2eq in 2000). Household consumption and food processing represented the most significant contributors to agrifood system emissions from cold chains, together representing in 2022 three-fourths of the total. These results align well with known global patterns of energy use in the refrigeration sector and highlight the importance of targeting cold chains within agrifood system with GHG emission mitigation actions. [ABSTRACT FROM AUTHOR]

Social contagion is a ubiquitous and fundamental process that drives individual and social changes. Although social contagion arises as a result of cognitive processes and biases, the integration of cognitive mechanisms with the theory of social contagion remains an open challenge. In particular, studies on social phenomena usually assume contagion dynamics to be either simple or complex, rather than allowing it to emerge from cognitive mechanisms, despite empirical evidence indicating that a social system can exhibit a spectrum of contagion dynamics--from simple to complex--simultaneously. Here, we propose a model of interacting beliefs, from which both simple and complex contagion dynamics can organically arise. Our model also elucidates how a fundamental mechanism of complex contagion--resistance--can come about from cognitive mechanisms. [ABSTRACT FROM AUTHOR]

The combustion of wood fuel for residential use is often not considered to be a source of greenhouse gas (GHG) emissions from households, as the emissions from wood fuel combustion can be offset by the CO2 absorbed by the growth of the forest (as a carbon sink) (IPCC, 2006). However, this only applies to wood that is harvested in a renewable way, i.e. at a rate not exceeding the regrowth rate of the forest from which it was harvested (Drigo et al., 2002). This paper estimates the share of GHG emissions attributable to non-renewable wood fuel harvesting for use in residential food activities, by country and with global coverage. It adds to a growing research base estimating GHG emissions from across the entire agri-food value chain, from the manufacture of farm inputs, through food supply chains, and finally to waste disposal (Tubiello et al., 2021). Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on wood fuel use by households. We find that, in 2019, annual emissions from non-renewable wood fuel consumed for household food preparation were about 745×106 t (Mt CO2eq. yr -1) , with an uncertainty ranging from - 63 % to + 64 %. Overall, global trends were a result of counterbalancing effects: the emission increases were largely fuelled by countries in sub-Saharan Africa, southern Asia, and Latin America, whereas significant decreases were seen in countries in eastern Asia and South-East Asia. The Food and Agriculture Organization of the United Nations (FAO) has developed and regularly maintains a database covering GHG emissions from the various components of the agri-food sector, including pre- and post-production activities, by country and world regions. The dataset has been developed according to the International Panel on Climate Change guidelines (IPCC, 2006), which avoid overlaps between agriculture, forestry, and other land use (AFOLU) and energy components. The aforementioned dataset relies mainly on UNSD Energy Statistics data, which are used as activity data for the calculation of the GHG emissions (Tubiello et al., 2022). The information used in this work is available as open data at 10.5281/zenodo.7310932 (Flammini et al., 2022a). [ABSTRACT FROM AUTHOR]

We propose a simple model of an idealized online cultural market in which N items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items’ popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings. We do so under the assumption that users, having limited time/attention, are able to discriminate the best-quality only within a random subset of the items. We discover the existence of a harmful regime in which improper use of popularity can seriously compromise the emergence of quality, and a benign regime in which wise use of popularity, coupled with a small discrimination effort on behalf of users, guarantees the perfect alignment of quality and popularity ranking. Our findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets. [ABSTRACT FROM AUTHOR]

The combustion of woodfuel for residential use is often not considered to be a source of greenhouse gas (GHG) emissions in households since emissions from woodfuel combustion can be offset by the CO2 absorbed by the growth of the forest as a carbon sink (IPCC, 2006). However, this only applies to wood that is harvested in a renewable way, i.e., at a rate not exceeding the regrowth rate of the forest from which it is harvested (Drigo et al., 2002). This paper estimates the share of GHG emissions attributable to non-renewable woodfuel harvesting for use in residential food activities. It adds to a growing research base estimating GHG emissions from across the entire agri-food value chain, from the manufacture of farm inputs, through food supply chains, and finally to waste disposal (Tubiello et al., 2021). Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on woodfuel use in households. We find that, in 2019, annual emissions from non-renewable woodfuel use in household food consumption were about 745 million tonnes (Mt CO2eq yr-1), with uncertainty ranging from -20 % to + 22 %, having increased 6% from 1990. Overall, global trends were a result of counterbalancing effects: the emission increases were largely fuelled from countries in Sub-Saharan Africa, Southern Asia, and Latin America while significant decreases were seen in countries in Eastern Asia and South-eastern Asia. The Food and Agriculture Organisation of the United Nations (FAO) has developed and regularly maintains a database covering GHG emissions from the various components of the agri-food sector, including pre- and post-production activities, by country and world regions. The dataset is developed according to International Panel on Climate Change guidelines (IPCC, 2006), which avoids overlaps across AFOLU and energy components. It relies mainly on UNSD Energy Statistics data, which are used as activity data for the calculation of the GHG emissions (Tubiello et al., 2022). The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.7310932 (Flammini et al., 2022). [ABSTRACT FROM AUTHOR]

Interpretation of empirical results based on a taxa's lifetime distribution shows apparently conflicting results. Species' lifetime is reported to be exponentially distributed, whereas higher-order taxa, such as families or genera, follow a broader distribution, compatible with power-law decay. We show that both forms of evidence are consistent with a simple evolutionary model that does not require specific assumptions on species interaction. The model provides a zero-order description of the dynamics of ecological communities, and its species lifetime distribution can be computed exactly. Different behaviors are found as follows: an initial [t.sup.-3/2] power law, emerging from a random walk type of dynamics, which crosses over to a steeper [t.sup.-2] branching process-like regime and finally is cut off by an exponential decay that becomes weaker and weaker as the total population increases. Sampling effects also can be taken into account and shown to be relevant. If species in the fossil record were sampled according to the Fisher log-series distribution, lifetime should be distributed according to a [t.sup.-1] power law. Such variability of behaviors in a simple model, combined with the scarcity of data available, casts serious doubt on the possibility of validating theories of evolution on the basis of species lifetime data. biodiversity | birth and death equations | branching processes | fossils analysis | stochastic processes

We present results from the FAOSTAT emissions shares database, covering emissions from agri-food systems and their shares to total anthropogenic emissions for 196 countries and 40 territories for the period 1990–2019. We find that in 2019, global agri-food system emissions were 16.5 (95 %; CI range: 11–22) billion metric tonnes (Gt CO 2 eq. yr -1), corresponding to 31 % (range: 19 %–43 %) of total anthropogenic emissions. Of the agri-food system total, global emissions within the farm gate – from crop and livestock production processes including on-farm energy use – were 7.2 Gt CO 2 eq. yr -1 ; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO 2 eq. yr -1 ; and emissions from pre- and post-production processes – manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal – were 5.8 Gt CO 2 eq. yr -1. Over the study period 1990–2019, agri-food system emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAOSTAT data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased 9 %. In 2019, in terms of individual greenhouse gases (GHGs), pre- and post-production processes emitted the most CO 2 (3.9 Gt CO 2 yr -1), preceding land use change (3.3 Gt CO 2 yr -1) and farm gate (1.2 Gt CO 2 yr -1) emissions. Conversely, farm gate activities were by far the major emitter of methane (140 Mt CH 4 yr -1) and of nitrous oxide (7.8 Mt N 2 O yr -1). Pre- and post-production processes were also significant emitters of methane (49 Mt CH 4 yr -1), mostly generated from the decay of solid food waste in landfills and open dumps. One key trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along the agri-food system, at global, regional and national scales. In fact, our data show that by 2019, pre- and post-production processes had overtaken farm gate processes to become the largest GHG component of agri-food system emissions in Annex I parties (2.2 Gt CO 2 eq. yr -1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO 2 eq. yr -1), becoming larger than emissions from land use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO 2 eq. yr -1), the USA (700 Mt CO 2 eq. yr -1) and the EU-27 (600 Mt CO 2 eq. yr -1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO 2 gases within the farm gate and on CO 2 mitigation from land use change. The information used in this work is available as open data with DOI 10.5281/zenodo.5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (https://www.fao.org/faostat/en/#data/EM ; FAO, 2021a), with annual updates. [ABSTRACT FROM AUTHOR]

Fossil-fuel-based energy use in agriculture leads to CO 2 and non-CO 2 emissions. We focus on emissions generated within the farm gate and from fisheries, providing information relative to the period 1970–2019, for both energy use, as input activity data and the associated greenhouse gas (GHG) emissions. Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on energy in agriculture (including forestry and fisheries), relative to use of gas/diesel oil, motor gasoline, liquefied petroleum gas (LPG), natural gas, fuel oil and coal. Electricity used within the farm gate is also quantified, while recognizing that the associated emissions are generated elsewhere. We find that, in 2019, annual emissions from energy use in agriculture were about 523 million tonnes (Mt CO 2 eq yr -1 ​​​​​​​), while when including electricity they were 1029 Mt CO 2 eq yr -1 , having increased 7 % from 1990. The largest emission increase from on-farm fuel combustion was from LPG (32 %), whereas significant decreases were observed for coal (-55 %), natural gas (-50 %), motor gasoline (-42 %) and fuel oil (-37 %). Conversely, the use of electricity and the associated indirect emissions increased 3-fold over the 1990–2019 period, thus becoming the largest emission source from energy use in agriculture since 2005. Overall, the global trends were a result of counterbalancing effects: marked decreases in developed countries in 2019 compared to 1990 (-273 Mt CO 2 eq yr -1) were masked by slightly larger increases in developing and emerging economies (+339 Mt CO 2 eq yr -1). The information used in this work is available as open data at https://doi.org/10.5281/zenodo.5153241 (Tubiello and Pan, 2021). The relevant Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) (FAO, 2021b) on emissions is maintained and updated annually by FAO. [ABSTRACT FROM AUTHOR]

We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries and territories and over the period 1990-2019. We find that in 2019, world-total food systems emissions were 16.5 billion metric tonnes (Gt CO2eq yr-1), corresponding to 31 % of total anthropogenic emissions. Of the agri-food systems total, global emissions within the farm gate -from crop and livestock production processes including on-farm energy use--were 7.2 Gt CO2eq yr-1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2eq yr-1; and emissions from pre- and post-production processes -manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal--were 5.8 Gt CO2eq yr-1. Over the study period 1990-2019, agri-food systems emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAO data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased only 9 %. In 2019, in terms of single GHG, pre- and post-production processes emitted the most CO2 (3.9 Gt CO2 yr-1), preceding land use change (3.3 Gt CO2 yr-1) and farm-gate (1.2 Gt CO2 yr-1) emissions. Conversely, farm-gate activities were by far the major emitter of methane (140 Mt CH4 yr-1) and of nitrous oxide (7.8 Mt N2O yr-1). Pre-and post-processes were also significant emitters of methane (49 Mt CH4 yr-1), mostly generated from the decay of solid food waste in landfills and open-dumps. The most important trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019. In fact, our data show that by 2019, food supply chains had overtaken farm-gate processes to become the largest GHG component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq yr-1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2eq yr-1), becoming larger than emissions from land-use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2eq yr-1); USA (700 Mt CO2eq yr-1) and EU-27 (600 Mt CO2eq yr-1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (FAO, 2021a), with annual updates. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: 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 [ABSTRACT FROM AUTHOR]

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles." [ABSTRACT FROM AUTHOR]

The increased relevance of social media in our daily life has been accompanied by efforts to manipulate online conversations and opinions. Deceptive social bots—automated or semi‐automated accounts designed to impersonate humans—have been successfully exploited for these kinds of abuse. Researchers have responded by developing artificial intelligence (AI) tools to arm the public in the fight against social bots. Here we review the literature on different types of bots, their impact, and detection methods. We use the case study of Botometer, a popular bot detection tool developed at Indiana University, to illustrate how people interact with AI countermeasures. A user experience survey suggests that bot detection has become an integral part of the social media experience for many users. However, barriers in interpreting the output of AI tools can lead to fundamental misunderstandings. The arms race between machine learning methods to develop sophisticated bots and effective countermeasures makes it necessary to update the training data and features of detection tools. We again use the Botometer case to illustrate both algorithmic and interpretability improvements of bot scores, designed to meet user expectations. We conclude by discussing how future AI developments may affect the fight between malicious bots and the public. [ABSTRACT FROM AUTHOR]

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-ofcustody, 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. [ABSTRACT FROM AUTHOR]

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network. [ABSTRACT FROM AUTHOR]

From politicians and nation states to terrorist groups, numerous organizations reportedly conduct explicit campaigns to influence opinions on social media, posing a risk to freedom of expression. Thus, there is a need to identify and eliminate "influence bots"--realistic, automated identities that illicitly shape discussions on sites like Twitter and Facebook--before they get too influential. [ABSTRACT FROM AUTHOR]

We refine the information available through the IPCC AR5 with regard to recent trends in global GHG emissions from agriculture, forestry and other land uses ( AFOLU), including global emission updates to 2012. Using all three available AFOLU datasets employed for analysis in the IPCC AR5, rather than just one as done in the IPCC AR5 WGIII Summary for Policy Makers, our analyses point to a down-revision of global AFOLU shares of total anthropogenic emissions, while providing important additional information on subsectoral trends. Our findings confirm that the share of AFOLU emissions to the anthropogenic total declined over time. They indicate a decadal average of 28.7 ± 1.5% in the 1990s and 23.6 ± 2.1% in the 2000s and an annual value of 21.2 ± 1.5% in 2010. The IPCC AR5 had indicated a 24% share in 2010. In contrast to previous decades, when emissions from land use (land use, land use change and forestry, including deforestation) were significantly larger than those from agriculture (crop and livestock production), in 2010 agriculture was the larger component, contributing 11.2 ± 0.4% of total GHG emissions, compared to 10.0 ± 1.2% of the land use sector. Deforestation was responsible for only 8% of total anthropogenic emissions in 2010, compared to 12% in the 1990s. Since 2010, the last year assessed by the IPCC AR5, new FAO estimates indicate that land use emissions have remained stable, at about 4.8 Gt CO2 eq yr−1 in 2012. Emissions minus removals have also remained stable, at 3.2 Gt CO2 eq yr−1 in 2012. By contrast, agriculture emissions have continued to grow, at roughly 1% annually, and remained larger than the land use sector, reaching 5.4 Gt CO2 eq yr−1 in 2012. These results are useful to further inform the current climate policy debate on land use, suggesting that more efforts and resources should be directed to further explore options for mitigation in agriculture, much in line with the large efforts devoted to REDD+ in the past decade. [ABSTRACT FROM AUTHOR]

A Sleeping Beauty (SB) in science refers to a paper whose importance is not recognized for several years after publication. Its citation history exhibits a long hibernation period followed by a sudden spike of popularity. Previous studies suggest a relative scarcity of SBs. The reliability of this conclusion is, however, heavily dependent on identification methods based on arbitrary threshold parameters for sleeping time and number of citations, applied to small or monodisciplinary bibliographic datasets. Here we present a systematic, large-scale, and multidisciplinary analysis of the SB phenomenon in science. We introduce a parameter-free measure that quantifies the extent to which a specific paper can be considered an SB. We apply our method to 22 million scientific papers published in all disciplines of natural and social sciences over a time span longer than a century. Our results reveal that the SB phenomenon is not exceptional. There is a continuous spectrum of delayed recognition where both the hibernation period and the awakening intensity are taken into account. Although many cases of SBs can be identified by looking at monodisciplinary bibliographic data, the SB phenomenon becomes much more apparent with the analysis of multidisciplinary datasets, where we can observe many examples of papers achieving delayed yet exceptional importance in disciplines different from those where they were originally published. Our analysis emphasizes a complex feature of citation dynamics that so far has received little attention, and also provides empirical evidence against the use of short-term citation metrics in the quantification of scientific impact. [ABSTRACT FROM AUTHOR]

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation. [ABSTRACT FROM AUTHOR]

We examine the temporal evolution of digital communication activity relating to the American anti-capitalist movement Occupy Wall Street. Using a high-volume sample from the microblogging site Twitter, we investigate changes in Occupy participant engagement, interests, and social connectivity over a fifteen month period starting three months prior to the movement's first protest action. The results of this analysis indicate that, on Twitter, the Occupy movement tended to elicit participation from a set of highly interconnected users with pre-existing interests in domestic politics and foreign social movements. These users, while highly vocal in the months immediately following the birth of the movement, appear to have lost interest in Occupy related communication over the remainder of the study period. [ABSTRACT FROM AUTHOR]

Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level. [ABSTRACT FROM AUTHOR]

In this paper we analyze the bipartite network of countries and products from UN data on country production. We define the country-country and product-product projected networks and introduce a novel method of filtering information based on elements' similarity. As a result we find that country clustering reveals unexpected socio-geographic links among the most competing countries. On the same footings the products clustering can be efficiently used for a bottom-up classification of produced goods. Furthermore we mathematically reformulate the "reflections method" introduced by Hidalgo and Hausmann as a fixpoint problem; such formulation highlights some conceptual weaknesses of the approach. To overcome such an issue, we introduce an alternative methodology (based on biased Markov chains) that allows to rank countries in a conceptually consistent way. Our analysis uncovers a strong non-linear interaction between the diversification of a country and the ubiquity of its products, thus suggesting the possible need of moving towards more efficient and direct non-linear fixpoint algorithms to rank countries and products in the global market. [ABSTRACT FROM AUTHOR]

Written text is one of the fundamental manifestations of human language, and the study of its universal regularities can give clues about how our brains process information and how we, as a society, organize and share it. Among these regularities, only Zipf's law has been explored in depth. Other basic properties, such as the existence of bursts of rare words in specific documents, have only been studied independently of each other and mainly by descriptive models. As a consequence, there is a lack of understanding of linguistic processes as complex emergent phenomena. Beyond Zipf's law for word frequencies, here we focus on burstiness, Heaps' law describing the sublinear growth of vocabulary size with the length of a document, and the topicality of document collections, which encode correlations within and across documents absent in random null models. We introduce and validate a generative model that explains the simultaneous emergence of all these patterns from simple rules. As a result, we find a connection between the bursty nature of rare words and the topical organization of texts and identify dynamic word ranking and memory across documents as key mechanisms explaining the non trivial organization of written text. Our research can have broad implications and practical applications in computer science, cognitive science and linguistics. [ABSTRACT FROM AUTHOR]