International audience ; Cet article étudie les pratiques de médiation informationnelle sur Facebook durant la campagne présidentielle française de 2017. Il se centre sur l'utilisation de la production de la presse quotidienne régionale (PQR) par les différentes communautés politiques. Il en ressort que les particularités éditoriales de ce type de presse sont exploitées par toutes les communautés mais que les sympathisants du Front national y ont davantage recours. Ils instrumentalisent le manque d'éditorialisation des faits divers produits par la PQR en les mobilisant pour servir leurs discours et leur idéologie politique.
International audience ; Cet article étudie les pratiques de médiation informationnelle sur Facebook durant la campagne présidentielle française de 2017. Il se centre sur l'utilisation de la production de la presse quotidienne régionale (PQR) par les différentes communautés politiques. Il en ressort que les particularités éditoriales de ce type de presse sont exploitées par toutes les communautés mais que les sympathisants du Front national y ont davantage recours. Ils instrumentalisent le manque d'éditorialisation des faits divers produits par la PQR en les mobilisant pour servir leurs discours et leur idéologie politique.
International audience ; In consecrating the lives of the deceased, obituaries offer a unique window into the values and social dynamics of academic communities. Here, we conduct a preliminary textual analysis of 5,069 obituaries published in The Lancet between 1850 and 2019 to understand how the genre has evolved in response to unfolding history and changing academic norms. We find that the rate of obituaries varied over time, peaking immediately following World War 1. On average, the sentiment of obituaries has increased over time. Largely, obituary text describes the life, accomplishments, and accomplishments of the deceased, although the prominence of these topics has changed over time. For example, discussion of military service was most prominent in the early 1900s, whereas more recent obituaries instead spend more time detailing the deceased's scholarship and academic career. Ours is the first large-scale text analysis of academic obituaries. In conducting this analysis, we revealed how this genre of writing has evolved over the past century in response to conflicts and changing conventions. Moving forward, we aim to leverage obituaries to better understand how academic virtues evolved, and how they differ by gender, discipline, and more.
International audience ; Cet article étudie les pratiques de médiation informationnelle sur Facebook durant la campagne présidentielle française de 2017. Il se centre sur l'utilisation de la production de la presse quotidienne régionale (PQR) par les différentes communautés politiques. Il en ressort que les particularités éditoriales de ce type de presse sont exploitées par toutes les communautés mais que les sympathisants du Front national y ont davantage recours. Ils instrumentalisent le manque d'éditorialisation des faits divers produits par la PQR en les mobilisant pour servir leurs discours et leur idéologie politique.
International audience ; In consecrating the lives of the deceased, obituaries offer a unique window into the values and social dynamics of academic communities. Here, we conduct a preliminary textual analysis of 5,069 obituaries published in The Lancet between 1850 and 2019 to understand how the genre has evolved in response to unfolding history and changing academic norms. We find that the rate of obituaries varied over time, peaking immediately following World War 1. On average, the sentiment of obituaries has increased over time. Largely, obituary text describes the life, accomplishments, and accomplishments of the deceased, although the prominence of these topics has changed over time. For example, discussion of military service was most prominent in the early 1900s, whereas more recent obituaries instead spend more time detailing the deceased's scholarship and academic career. Ours is the first large-scale text analysis of academic obituaries. In conducting this analysis, we revealed how this genre of writing has evolved over the past century in response to conflicts and changing conventions. Moving forward, we aim to leverage obituaries to better understand how academic virtues evolved, and how they differ by gender, discipline, and more.
In social processes, long-term trends can be influenced or disrupted by various factors, including public policy. When public policies depend on a misrepresentation of trends in the areas they are aimed at, they become random and disruptive, which can be interpreted as a source of disorder. Here we consider policies on the spatial organization of the French Higher Education and Research system, which reflects the authorities' hypothesis that scientific excellence is the prerogative of a few large urban agglomerations. By geographically identifying all the French publications listed in the Web of Science databases between 1999 and 2017, we highlight a spatial deconcentration trend, which has slowed down in recent years due to a freezed growth of the teaching force. This deconcentration continues, however, to sustain the growth of scientific production in small and medium-sized towns. An examination of the large conurbations shows the relative decline of sites that nevertheless have been highlighted as examples to be followed by the Excellence policies (Strasbourg among others). The number of students and faculty has grown less there, and it is a plaussible explanation for the relative decline in scientific production. We show that the publication output of a given site depends directly and strongly on the number of researchers hosted there. Based on precise data at the French level, our results confirm what is already known at world scale. In conclusion, we question the amount of disorder resulting from policies aligned with poorly assessed trends. ; Dans les processus sociaux, les tendances de long terme peuvent être infléchies ou perturbées par différents facteurs, parmi lesquels figurent les politiques publiques. Lorsque les politiques publiques se fondent sur une représentation erronée des tendances à l'oeuvre dans les domaines qu'elles visent, elles prennent un caractère aléatoire et perturbateur, que l'on peut interpréter comme une source de désordre. Dans ce texte, nous prenons le cas des politiques ...
In social processes, long-term trends can be influenced or disrupted by various factors, including public policy. When public policies depend on a misrepresentation of trends in the areas they are aimed at, they become random and disruptive, which can be interpreted as a source of disorder. Here we consider policies on the spatial organization of the French Higher Education and Research system, which reflects the authorities' hypothesis that scientific excellence is the prerogative of a few large urban agglomerations. By geographically identifying all the French publications listed in the Web of Science databases between 1999 and 2017, we highlight a spatial deconcentration trend, which has slowed down in recent years due to a freezed growth of the teaching force. This deconcentration continues, however, to sustain the growth of scientific production in small and medium-sized towns. An examination of the large conurbations shows the relative decline of sites that nevertheless have been highlighted as examples to be followed by the Excellence policies (Strasbourg among others). The number of students and faculty has grown less there, and it is a plaussible explanation for the relative decline in scientific production. We show that the publication output of a given site depends directly and strongly on the number of researchers hosted there. Based on precise data at the French level, our results confirm what is already known at world scale. In conclusion, we question the amount of disorder resulting from policies aligned with poorly assessed trends. ; Dans les processus sociaux, les tendances de long terme peuvent être infléchies ou perturbées par différents facteurs, parmi lesquels figurent les politiques publiques. Lorsque les politiques publiques se fondent sur une représentation erronée des tendances à l'oeuvre dans les domaines qu'elles visent, elles prennent un caractère aléatoire et perturbateur, que l'on peut interpréter comme une source de désordre. Dans ce texte, nous prenons le cas des politiques ...
In social processes, long-term trends can be influenced or disrupted by various factors, including public policy. When public policies depend on a misrepresentation of trends in the areas they are aimed at, they become random and disruptive, which can be interpreted as a source of disorder. Here we consider policies on the spatial organization of the French Higher Education and Research system, which reflects the authorities' hypothesis that scientific excellence is the prerogative of a few large urban agglomerations. By geographically identifying all the French publications listed in the Web of Science databases between 1999 and 2017, we highlight a spatial deconcentration trend, which has slowed down in recent years due to a freezed growth of the teaching force. This deconcentration continues, however, to sustain the growth of scientific production in small and medium-sized towns. An examination of the large conurbations shows the relative decline of sites that nevertheless have been highlighted as examples to be followed by the Excellence policies (Strasbourg among others). The number of students and faculty has grown less there, and it is a plaussible explanation for the relative decline in scientific production. We show that the publication output of a given site depends directly and strongly on the number of researchers hosted there. Based on precise data at the French level, our results confirm what is already known at world scale. In conclusion, we question the amount of disorder resulting from policies aligned with poorly assessed trends. ; Dans les processus sociaux, les tendances de long terme peuvent être infléchies ou perturbées par différents facteurs, parmi lesquels figurent les politiques publiques. Lorsque les politiques publiques se fondent sur une représentation erronée des tendances à l'oeuvre dans les domaines qu'elles visent, elles prennent un caractère aléatoire et perturbateur, que l'on peut interpréter comme une source de désordre. Dans ce texte, nous prenons le cas des politiques ...
In social processes, long-term trends can be influenced or disrupted by various factors, including public policy. When public policies depend on a misrepresentation of trends in the areas they are aimed at, they become random and disruptive, which can be interpreted as a source of disorder. Here we consider policies on the spatial organization of the French Higher Education and Research system, which reflects the authorities' hypothesis that scientific excellence is the prerogative of a few large urban agglomerations. By geographically identifying all the French publications listed in the Web of Science databases between 1999 and 2017, we highlight a spatial deconcentration trend, which has slowed down in recent years due to a freezed growth of the teaching force. This deconcentration continues, however, to sustain the growth of scientific production in small and medium-sized towns. An examination of the large conurbations shows the relative decline of sites that nevertheless have been highlighted as examples to be followed by the Excellence policies (Strasbourg among others). The number of students and faculty has grown less there, and it is a plaussible explanation for the relative decline in scientific production. We show that the publication output of a given site depends directly and strongly on the number of researchers hosted there. Based on precise data at the French level, our results confirm what is already known at world scale. In conclusion, we question the amount of disorder resulting from policies aligned with poorly assessed trends. ; Dans les processus sociaux, les tendances de long terme peuvent être infléchies ou perturbées par différents facteurs, parmi lesquels figurent les politiques publiques. Lorsque les politiques publiques se fondent sur une représentation erronée des tendances à l'oeuvre dans les domaines qu'elles visent, elles prennent un caractère aléatoire et perturbateur, que l'on peut interpréter comme une source de désordre. Dans ce texte, nous prenons le cas des politiques ...
In social processes, long-term trends can be influenced or disrupted by various factors, including public policy. When public policies depend on a misrepresentation of trends in the areas they are aimed at, they become random and disruptive, which can be interpreted as a source of disorder. Here we consider policies on the spatial organization of the French Higher Education and Research system, which reflects the authorities' hypothesis that scientific excellence is the prerogative of a few large urban agglomerations. By geographically identifying all the French publications listed in the Web of Science databases between 1999 and 2017, we highlight a spatial deconcentration trend, which has slowed down in recent years due to a freezed growth of the teaching force. This deconcentration continues, however, to sustain the growth of scientific production in small and medium-sized towns. An examination of the large conurbations shows the relative decline of sites that nevertheless have been highlighted as examples to be followed by the Excellence policies (Strasbourg among others). The number of students and faculty has grown less there, and it is a plaussible explanation for the relative decline in scientific production. We show that the publication output of a given site depends directly and strongly on the number of researchers hosted there. Based on precise data at the French level, our results confirm what is already known at world scale. In conclusion, we question the amount of disorder resulting from policies aligned with poorly assessed trends. ; Dans les processus sociaux, les tendances de long terme peuvent être infléchies ou perturbées par différents facteurs, parmi lesquels figurent les politiques publiques. Lorsque les politiques publiques se fondent sur une représentation erronée des tendances à l'oeuvre dans les domaines qu'elles visent, elles prennent un caractère aléatoire et perturbateur, que l'on peut interpréter comme une source de désordre. Dans ce texte, nous prenons le cas des politiques ...
Numerous domains have interests in studying the viewpoints expressed online, be it for marketing, cybersecurity, or research purposes with the rise of computational social sciences. Current stance detection models are usually grounded on the specificities of some social platforms. This rigidity is unfortunate since it does not allow the integration of the multitude of signals informing effective stance detection. We propose the SCSD model, or Sequential Community-based Stance Detection model, a semi-supervised ensemble algorithm which considers these signals by modeling them as a multi-layer graph representing proximities between profiles. We use a handful of seed profiles, for whom we know the stance, to classify the rest of the profiles by exploiting like-minded communities. These communities represent profiles close enough to assume they share a similar stance on a given subject. Using datasets from two different social platforms, containing two to five stances, we show that by combining several types of proximity we can achieve excellent results. Moreover, we compare the proximities to find those which convey useful information in term of stance detection.
International audience ; Numerous domains have interests in studying the viewpoints expressed online, be it for marketing, cybersecurity, or research purposes with the rise of computational social sciences. Current stance detection models are usually grounded on the specificities of some social platforms. This rigidity is unfortunate since it does not allow the integration of the multitude of signals informing effective stance detection. We propose the SCSD model, or Sequential Community-based Stance Detection model, a semi-supervised ensemble algorithm which considers these signals by modeling them as a multi-layer graph representing proximities between profiles. We use a handful of seed profiles, for whom we know the stance, to classify the rest of the profiles by exploiting like-minded communities. These communities represent profiles close enough to assume they share a similar stance on a given subject. Using datasets from two different social platforms, containing two to five stances, we show that by combining several types of proximity we can achieve excellent results. Moreover, we compare the proximities to find those which convey useful information in term of stance detection.
The French presidential election was one of the main political event of 2017, and triggered a lot of activity on Twitter. The campaign was highly unpredictable and led to the rise of 5 main parties instead of the historical bipartite (left-right) confrontation, ranging from far-left to far-right. This dataset paper proposes #Élysée2017fr, a large and complex dataset of 22853 Twitter profiles active during the campaign (from November 2016 to May 2017), and their corresponding tweets and retweets, plus the retweet and mention networks related to these profiles. The profiles were manually annotated with their political affiliations (up to 2 political parties per profile), their nature (individual or collective), and the sex of the profile's owner when available. This is one of the rare datasets that considers a non-binary stance classification and, to our knowledge, the first one with a large number of profiles, and the first one proposing overlapping political communities. This dataset can be used as-is to study the campaign mechanisms on Twitter, or used to test stance detection models or network analysis tools. Mining these data might reveal new insights on current issues like echo chambers or fake news diffusion.
International audience ; The French presidential election was one of the main political event of 2017, and triggered a lot of activity on Twitter. The campaign was highly unpredictable and led to the rise of 5 main parties instead of the historical bipartite (left-right) confrontation, ranging from far-left to far-right. This dataset paper proposes #Élysée2017fr, a large and complex dataset of 22853 Twitter profiles active during the campaign (from November 2016 to May 2017), and their corresponding tweets and retweets, plus the retweet and mention networks related to these profiles. The profiles were manually annotated with their political affiliations (up to 2 political parties per profile), their nature (individual or collective), and the sex of the profile's owner when available. This is one of the rare datasets that considers a non-binary stance classification and, to our knowledge, the first one with a large number of profiles, and the first one proposing overlapping political communities. This dataset can be used as-is to study the campaign mechanisms on Twitter, or used to test stance detection models or network analysis tools. Mining these data might reveal new insights on current issues like echo chambers or fake news diffusion.
International audience ; The French presidential election was one of the main political event of 2017, and triggered a lot of activity on Twitter. The campaign was highly unpredictable and led to the rise of 5 main parties instead of the historical bipartite (left-right) confrontation, ranging from far-left to far-right. This dataset paper proposes #Élysée2017fr, a large and complex dataset of 22853 Twitter profiles active during the campaign (from November 2016 to May 2017), and their corresponding tweets and retweets, plus the retweet and mention networks related to these profiles. The profiles were manually annotated with their political affiliations (up to 2 political parties per profile), their nature (individual or collective), and the sex of the profile's owner when available. This is one of the rare datasets that considers a non-binary stance classification and, to our knowledge, the first one with a large number of profiles, and the first one proposing overlapping political communities. This dataset can be used as-is to study the campaign mechanisms on Twitter, or used to test stance detection models or network analysis tools. Mining these data might reveal new insights on current issues like echo chambers or fake news diffusion.
