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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. 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. 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. 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.

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.

The rise of climate denialism in the general population ( + 5% between 2019 and 2023) has been accompanied by a very significant increase in denialist activism on “X”/Twitter since the summer of 2022, and increased hostility towards climate scientists. Through global tracking of Twitter exchanges about climate change between 2019 and 2023, as well as exchanges about COVID-19 pandemics, we analyzed this online trend and its interaction with other societal issues like politics and COVID-19 pandemics. Beyond fact-checking, we show, through complex networks and semantic analyses, that there are structural differences between these denialist and pro-climate online communities, as well as between the circulation of false information and other climate change-related narratives. All the evidence suggests that the behavior of deniers is designed to deceive, and that they are over-represented on social networks compared to what they actually represent offline. This is particularly true on “X”/Twitter since Musk’s takeover. We have also highlighted the globalized aspect of this new denialism, its alignment with the interests and visions of powers such as Russia and how it has benefited from the COVID-19 pandemic.

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. 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. 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. 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.

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 \\textit{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. Contexte Les espaces numériques, et en particulier les sites de réseaux sociaux, sont de plus en plus présents et influents dans le fonctionnement de nos démocraties. Dans cet article, nous proposons une méthodologie intégrée pour la collecte de données, la reconstruction, l’analyse et la visualisation du développement du paysage politique d’un pays à partir des données de Twitter.Methode La méthode proposée repose uniquement sur les interactions entre les comptes Twitter et est indépendante des caractéristiques des contenus partagés tels que la langue des tweets. Nous validons notre méthodologie sur une étude de cas portant sur l’élection présidentielle française de 2017 (60 millions d’échanges Twitter entre plus de 2,4 millions d’utilisateurs) via deux méthodes indépendantes : la comparaison entre notre catégorisation politique automatisée et une catégorisation humaine basée sur l’évaluation d’un échantillon de 5000 descriptions de profils ; et la correspondance entre les reconfigurations détectées dans le paysage politique reconstruit et les événements politiques clés rapportés dans les médias. Cette dernière validation démontre la capacité de notre approche à refléter avec précision les reconfigurations de la scène politique d’un pays à partir des données des réseaux sociaux.Résultats Nous nous sommes appuyés sur cette reconstruction pour donner un aperçu des dynamiques d’opinion et de la reconfiguration des communautés politiques qui sont en jeu lors d’une élection présidentielle. Tout d’abord, nous proposons une description et une analyse quantitative de l’engagement politique des membres des communautés politiques. Ensuite, nous analysons l’impact des communautés politiques sur la diffusion de l’information et en particulier sur leur rôle dans le phénomène des fausses nouvelles. Nous mesurons un effet différentiel de chambre d’écho (echo chamber) sur les différents types de nouvelles politiques (fausses nouvelles, démentis, nouvelles standards) causées par la structure communautaire. Nous soulignons l’importance de prendre en compte les méso-structures des réseaux politiques pour comprendre les phénomènes de type “fausses nouvelles” (fake news).Conclusions En donnant accès à un niveau intermédiaire, entre les enquêtes sociologiques de terrain et les grandes études statistiques (telles que celles menées par des organisations nationales ou internationales), nous démontrons que les données des réseaux sociaux permettent de qualifier et de quantifier l’activité des communautés politiques dans un environnement politique multipolaire, ainsi que leur évolution et reconfiguration temporelle, leur structure, leurs stratégies d’alliance et leurs particularités sémantiques au cours d’une campagne présidentielle à travers l’analyse de leurs traces numériques. Nous concluons ce document par un commentaire sur les implications politiques et éthiques de l’utilisation des données des réseaux sociaux en politique. Nous insistons sur l’importance de développer des macroscopes sociaux qui permettront aux citoyens de mieux comprendre la manière dont collectivement ils font société. Nous proposons comme exemple le \\textit{Politoscope}, un macroscope qui restitue certains de nos résultats d’une manière interactive.

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. 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. 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. 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.

As the last few years have seen an increase in online hostility and polarization both, we need to move beyond the fack-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed at large scale by digital platforms such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive biais on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric and to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to an overexposure of users to negative content (up to 300\\% for some of them), a phenomenon called algorithmic negativity bias, to a polarization of the opinion landscape, and to a concentration of social power in the hands of the most toxic users. The latter are more than twice as numerous in the top 1\\% of the most influential users than in the overall population. Overall, our findings highlight the urgency to identify harmful implementations of recommender systems to individuals and society in order better regulate their deployment on systemic SNSs.

In this report, we introduce INTELLECT-1, the first 10 billion parameter language model collaboratively trained across the globe, demonstrating that large-scale model training is no longer confined to large corporations but can be achieved through a distributed, community-driven approach. INTELLECT-1 was trained on 1 trillion tokens using up to 14 concurrent nodes distributed across 3 continents, with contributions from 30 independent compute providers dynamically joining and leaving the training process, while maintaining 83-96% compute utilization and 36.2-41.4% model FLOPS utilization. We leverage PRIME, our scalable distributed training framework designed for fault-tolerant, high-performance training on unreliable, globally distributed nodes. Key innovations in PRIME include the ElasticDeviceMesh, which manages dynamic global process groups for fault-tolerant communication across the internet and local process groups for communication within a node, live checkpoint recovery kernels, and a hybrid DiLoCo-FSDP2 implementation. Using PRIME with DiLoCo and our custom int8 all-reduce, we achieve a 400x reduction in communication bandwidth compared to traditional data-parallel training settings while delivering comparable performance. These results demonstrate the feasibility and promise of training frontier foundation models in a decentralized network of global GPU resources.