Crowdsourced audit of Twitter's recommender systems.

This research conducts an audit of Twitter's recommender system, aiming to examine the disparities between users' curated timelines and their subscription choices. Through the combined use of a browser extension and data collection via the Twitter API, our investigation reveals a high amplification of friends from the same community, a preference for amplifying emotionally charged and toxic tweets and an uneven algorithmic amplification across friends' political leaning. This audit emphasizes the importance of transparency, and increased awareness regarding the impact of algorithmic curation.

Toward an observatory of the evolution of clinical trials through phylomemy reconstruction: the COVID-19 vaccines example.

OBJECTIVES: To visualize the evolution of all registered COVID-19 vaccine trials. STUDY DESIGN AND SETTING: As part of the living mapping of the COVID-NMA initiative, we identify biweekly all COVID-19 vaccine trials and automatically extract data from the EU clinical trials registry, ClinicalTrials.gov, IRCT and the World Health Organization International Clinical Trials Registry Platform. Data are curated and enriched by epidemiologists. We have used the phylomemy reconstruction process to visualize the temporal evolution of COVID-19 vaccines trials descriptions. We have analyzed the textual contents of 1,794 trials descriptions (last search in October 2021) and explored their collective structure along with their semantic dynamics. RESULTS: The structures highlighted by the phylomemy reconstruction processes synthesize the complexity of the knowledge produced by the research community. The reconstructed phylomemy clearly retrieves the five major COVID-19 vaccine platforms in the form of complete branches. The branches interactions reflect the exploration of a new approach to vaccine implementation moving from homologous prime vaccination to heterologous prime vaccination. Phylomemies also clearly identifies shifts in research questions, from vaccine efficacy to booster efficacy. CONCLUSION: This new method provides important insights for the global coordination between research teams especially in crisis situations such as the COVID-19 pandemic.

Draw me Science: Multi-level and multi-scale reconstruction of knowledge dynamics with phylomemies.

In 1751, Jean le Rond d'Alembert had a dream: "to make a genealogical or encyclopedic tree which will gather the various branches of knowledge together under a single point of view and will serve to indicate their origin and their relationships to one another". In this paper, we address the question identifying the branches of science by taking advantage of the massive digitization of scientific production. In the framework of complex systems studies, we first formalize the notion of level and scale of knowledge dynamics. Then, we demonstrate how we can reconstruct a reasonably precise and concise multi-scale and multi-level approximation of the dynamical structures of Science: phylomemies. We introduce the notion of phylomemetic networks-projections of phylomemies in low dimensional spaces that can be grasped by the human mind-and propose a new algorithm to reconstruct both phylomemies and the associated phylomemetic networks. This algorithm offers, passing, a new temporal clustering on evolving semantic networks. Last, we show how phylomemy reconstruction can take into account users' preferences within the framework of embodied cognition, thus defining a third way between the quest for objective "ground truth" and the ad-hoc adaptation to a particular user's preferences. The robustness of this approach is illustrated by several case studies. Supplementary Information: The online version contains supplementary material available at 10.1007/s11192-021-04186-5.

From inert matter to the global society life as multi-level networks of processes.

A few billion years have passed since the first life forms appeared. Since then, life has continued to forge complex associations between the different emergent levels of interconnection it forms. The advances of recent decades in molecular chemistry and theoretical biology, which have embraced complex systems approaches, now make it possible to conceptualize the questions of the origins of life and its increasing complexity from three complementary notions of closure: processes closure, autocatalytic closure and constraints closure. Developed in the wake of the second-order cybernetics, this triple closure approach, that relies on graph theory and complex networks science, sketch a paradigm where it is possible to go up the physical levels of organization of matter, from physics to biology and society, without resorting to strong reductionism. The phenomenon of life is conceived as the contingent complexification of the organization of matter, until the emergence of life forms, defined as a network of auto-catalytic process networks, organized in a multi-level manner. This approach of living systems, initiated by Maturana & Varela and Kauffman, inevitably leads to a reflection on the nature of cognition; and in the face of the deep changes that affected humanity as a complex systems, on the nature of cultural evolution. Faced with the major challenges that humanity will have to address in the decades to come, this new paradigm invites us to change our conception of causality by shifting our attention from state change to process change and to abandon a widespread notion of 'local' causality in favour of complex systems thinking. It also highlights the importance of a better understanding of the influence of social networks, recommendation systems and artificial intelligence on our future collective dynamics and social cognition processes. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Mobile-based traceability system for sustainable food supply networks.

Traceability is key to ensure food quality and safety from farm to fork, yet high implementation costs and the complexity of the food supply chain pose challenges to its operation. Here we propose a mobile-based bidirectional tracing system for food products that integrates graph data and peer-to-peer architecture. Our system allows data synchronization to happen seamlessly between all connected nodes, as data are gathered through market transactions and all related product information is concatenated by scanning 2D product barcodes. The system's decentralized and flexible structure favours stakeholder involvement and is applicable to various and dynamic food networks. By promoting resource efficiency and transparency of origin, production and distribution, the system ensures mesh surveillance and sheds light on complex food networks, ultimately contributing to the advancement of food research.

Reconstruction of the socio-semantic dynamics of political activist Twitter networks-Method and application to the 2017 French presidential election.

BACKGROUND: Digital spaces, and in particular social networking sites, are becoming increasingly present and influential in the functioning of our democracies. In this paper, we propose an integrated methodology for the data collection, the reconstruction, the analysis and the visualization of the development of a country's political landscape from Twitter data. METHOD: The proposed method relies solely on the interactions between Twitter accounts and is independent of the characteristics of the shared contents such as the language of the tweets. We validate our methodology on a case study on the 2017 French presidential election (60 million Twitter exchanges between more than 2.4 million users) via two independent methods: the comparison between our automated political categorization and a human categorization based on the evaluation of a sample of 5000 profiles descriptions; the correspondence between the reconfigurations detected in the reconstructed political landscape and key political events reported in the media. This latter validation demonstrated the ability of our approach to accurately reflect the reconfigurations at play in the off-line political scene. RESULTS: We built on this reconstruction to give insights into the opinion dynamics and the reconfigurations of political communities at play during a presidential election. First, we propose a quantitative description and analysis of the political engagement of members of political communities. Second, we analyze the impact of political communities on information diffusion and in particular on their role in the fake news phenomena. We measure a differential echo chamber effect on the different types of political news (fake news, debunks, standard news) caused by the community structure and emphasize the importance of addressing the meso-structures of political networks in understanding the fake news phenomena. CONCLUSIONS: Giving access to an intermediate level, between sociological surveys in the field and large statistical studies (such as those conducted by national or international organizations) we demonstrate that social networks data make it possible to qualify and quantify the activity of political communities in a multi-polar political environment; as well as their temporal evolution and reconfiguration, their structure, their alliance strategies and their semantic particularities during a presidential campaign through the analysis of their digital traces. We conclude this paper with a comment on the political and ethical implications of the use of social networks data in politics. We stress the importance of developing social macroscopes that will enable citizens to better understand how they collectively make society and propose as example the "Politoscope", a macroscope that delivers some of our results in an interactive way.

Evolution of Reporting P Values in the Biomedical Literature, 1990-2015.

IMPORTANCE: The use and misuse of P values has generated extensive debates. OBJECTIVE: To evaluate in large scale the P values reported in the abstracts and full text of biomedical research articles over the past 25 years and determine how frequently statistical information is presented in ways other than P values. DESIGN: Automated text-mining analysis was performed to extract data on P values reported in 12,821,790 MEDLINE abstracts and in 843,884 abstracts and full-text articles in PubMed Central (PMC) from 1990 to 2015. Reporting of P values in 151 English-language core clinical journals and specific article types as classified by PubMed also was evaluated. A random sample of 1000 MEDLINE abstracts was manually assessed for reporting of P values and other types of statistical information; of those abstracts reporting empirical data, 100 articles were also assessed in full text. MAIN OUTCOMES AND MEASURES: P values reported. RESULTS: Text mining identified 4,572,043 P values in 1,608,736 MEDLINE abstracts and 3,438,299 P values in 385,393 PMC full-text articles. Reporting of P values in abstracts increased from 7.3% in 1990 to 15.6% in 2014. In 2014, P values were reported in 33.0% of abstracts from the 151 core clinical journals (n = 29,725 abstracts), 35.7% of meta-analyses (n = 5620), 38.9% of clinical trials (n = 4624), 54.8% of randomized controlled trials (n = 13,544), and 2.4% of reviews (n = 71,529). The distribution of reported P values in abstracts and in full text showed strong clustering at P values of .05 and of .001 or smaller. Over time, the "best" (most statistically significant) reported P values were modestly smaller and the "worst" (least statistically significant) reported P values became modestly less significant. Among the MEDLINE abstracts and PMC full-text articles with P values, 96% reported at least 1 P value of .05 or lower, with the proportion remaining steady over time in PMC full-text articles. In 1000 abstracts that were manually reviewed, 796 were from articles reporting empirical data; P values were reported in 15.7% (125/796 [95% CI, 13.2%-18.4%]) of abstracts, confidence intervals in 2.3% (18/796 [95% CI, 1.3%-3.6%]), Bayes factors in 0% (0/796 [95% CI, 0%-0.5%]), effect sizes in 13.9% (111/796 [95% CI, 11.6%-16.5%]), other information that could lead to estimation of P values in 12.4% (99/796 [95% CI, 10.2%-14.9%]), and qualitative statements about significance in 18.1% (181/1000 [95% CI, 15.8%-20.6%]); only 1.8% (14/796 [95% CI, 1.0%-2.9%]) of abstracts reported at least 1 effect size and at least 1 confidence interval. Among 99 manually extracted full-text articles with data, 55 reported P values, 4 presented confidence intervals for all reported effect sizes, none used Bayesian methods, 1 used false-discovery rates, 3 used sample size/power calculations, and 5 specified the primary outcome. CONCLUSIONS AND RELEVANCE: In this analysis of P values reported in MEDLINE abstracts and in PMC articles from 1990-2015, more MEDLINE abstracts and articles reported P values over time, almost all abstracts and articles with P values reported statistically significant results, and, in a subgroup analysis, few articles included confidence intervals, Bayes factors, or effect sizes. Rather than reporting isolated P values, articles should include effect sizes and uncertainty metrics.

Phylomemetic patterns in science evolution--the rise and fall of scientific fields.

We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

Science mapping analysis characterizes 235 biases in biomedical research.

OBJECTIVE: Many different types of bias have been described. Some biases may tend to coexist or be associated with specific research settings, fields, and types of studies. We aimed to map systematically the terminology of bias across biomedical research. STUDY DESIGN AND SETTING: We used advanced text-mining and clustering techniques to evaluate 17,265,924 items from PubMed (1958-2008). We considered 235 bias terms and 103 other terms that appear commonly in articles dealing with bias. RESULTS: Forty bias terms were used in the title or abstract of more than 100 articles each. Pseudo-inclusion clustering identified 252 clusters of terms. The clusters were organized into macroscopic maps that cover a continuum of research fields. The resulting maps highlight which types of biases tend to co-occur and may need to be considered together and what biases are commonly encountered and discussed in specific fields. Most of the common bias terms have had continuous use over time since their introduction, and some (in particular confounding, selection bias, response bias, and publication bias) show increased usage through time. CONCLUSION: This systematic mapping offers a dynamic classification of biases in biomedical investigation and related fields and can offer insights for the multifaceted aspects of bias.