170 research outputs found
Détection de points de vue sur les médias sociaux numériques
De nombreux domaines ont intérêt à étudier les points de vue exprimés en ligne, que ce soit à des fins de marketing, de
cybersécurité ou de recherche avec l'essor des humanités numériques.
Nous proposons dans ce manuscrit deux contributions au domaine de la fouille de points de vue, axées sur la difficulté à
obtenir des données annotées de qualité sur les médias sociaux.
Notre première contribution est un jeu de données volumineux et complexe de 22853 profils Twitter actifs durant la campagne
présidentielle française de 2017.
C'est l'un des rares jeux de données considérant plus de deux points de vue et, à notre connaissance, le premier avec un
grand nombre de profils et le premier proposant des communautés politiques recouvrantes.
Ce jeu de données peut être utilisé tel quel pour étudier les mécanismes de campagne sur Twitter ou pour évaluer des modèles
de détection de points de vue ou des outils d'analyse de réseaux.
Nous proposons ensuite deux modèles génériques semi-supervisés de détection de points de vue, utilisant une poignée de
profils-graines, pour lesquels nous connaissons le point de vue, afin de catégoriser le reste des profils en exploitant
différentes proximités inter-profils.
En effet, les modèles actuels sont généralement fondés sur les spécificités de certaines plateformes sociales, ce qui ne
permet pas l'intégration de la multitude de signaux disponibles.
En construisant des proximités à partir de différents types d'éléments disponibles sur les médias sociaux, nous pouvons
détecter des profils suffisamment proches pour supposer qu'ils partagent une position similaire sur un sujet donné, quelle
que soit la plateforme.
Notre premier modèle est un modèle ensembliste séquentiel propageant les points de vue grâce à un graphe multicouche
représentant les proximités entre les profils.
En utilisant des jeux de données provenant de deux plateformes, nous montrons qu'en combinant plusieurs types de proximité,
nous pouvons correctement étiqueter 98% des profils.
Notre deuxième modèle nous permet d'observer l'évolution des points de vue des profils pendant un événement, avec seulement
un profil-graine par point de vue.
Ce modèle confirme qu'une grande majorité de profils ne changent pas de position sur les médias sociaux, ou n'expriment pas leur revirement.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.
We propose in this manuscript two contributions to the field of stance detection, focused around the difficulty of obtaining
annotated data of quality on social medias.
Our first contribution is a large and complex dataset of 22853 Twitter profiles active during the French presidential
campaign of 2017.
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.
We then propose two semi-supervised generic stance detection models using a handful of seed profiles for which we know the
stance to classify the rest of the profiles by exploiting various proximities.
Indeed, current stance detection models are usually grounded on the specificities of some social platforms, which is
unfortunate since it does not allow the integration of the multitude of available signals.
By infering proximities from differents types of elements available on social medias, we can detect profiles close enough to
assume they share a similar stance on a given subject.
Our first model is a sequential ensemble algorithm which propagates stances thanks to a multi-layer graph representing
proximities between profiles.
Using datasets from two platforms, we show that, by combining several types of proximities, we can achieve excellent results.
Our second model allows us to observe the evolution of profiles' stances during an event with as little as one seed profile
by stance.
This model confirms that a large majority of profiles do not change their stance on social medias, or do not express their change of heart
Altered Protein Networks and Cellular Pathways in Severe West Nile Disease in Mice
Background:The recent West Nile virus (WNV) outbreaks in developed countries, including Europe and the United States, have been associated with significantly higher neuropathology incidence and mortality rate than previously documented. The changing epidemiology, the constant risk of (re-)emergence of more virulent WNV strains, and the lack of effective human antiviral therapy or vaccines makes understanding the pathogenesis of severe disease a priority. Thus, to gain insight into the pathophysiological processes in severe WNV infection, a kinetic analysis of protein expression profiles in the brain of WNV-infected mice was conducted using samples prior to and after the onset of clinical sympt
Kinetic analysis of mouse brain proteome alterations following chikungunya virus infection before and after appearance of clinical symptoms
Recent outbreaks of Chikungunya virus (CHIKV) infection have been characterized by an increasing number of severe cases with atypical manifestations including neurological complications. In parallel, the risk map of CHIKV outbreaks has expanded because of improved vector competence. These features make CHIKV infection a major public health concern that requires a better understanding of the underlying physiopathological processes for the development of antiviral strategies to protect individuals from severe disease. To decipher the mechanisms of CHIKV in
Cerebrospinal fluid biomarker candidates associated with human WNV neuroinvasive disease
During the last decade, the epidemiology of WNV in humans has changed in the southern regions of Europe, with high incidence of West Nile fever (WNF) cases, but also of West Nile neuroinvasive disease (WNND). The lack of human vaccine or specific treatment against WNV infection imparts a pressing need to characterize indicators associated with neurological involvement. By its intimacy with central nervous system (CNS) structures, modifications in the cerebrospinal fluid (CSF) composition could accurately reflect CNS pathological process. Until now, few studies investigated the association between imbalance of CSF elements and severity of WNV infection. The aim of the present study was to apply the iTRAQ technology in order to identify the CSF proteins whose abundances are modified in patients with WNND. Forty-seven proteins were found modified in the CSF of WNND patients as compared to control groups, and most of them are reported for the first time in the context of WNND. On the basis of their known biological functions, several of these proteins were associated with inflammatory response. Among them, Defensin-1 alpha (DEFA1), a protein reported with anti-viral effects, presente
Stance detection on social media: State of the art and trends
Stance detection on social media is an emerging opinion mining paradigm for
various social and political applications in which sentiment analysis may be
sub-optimal. There has been a growing research interest for developing
effective methods for stance detection methods varying among multiple
communities including natural language processing, web science, and social
computing. This paper surveys the work on stance detection within those
communities and situates its usage within current opinion mining techniques in
social media. It presents an exhaustive review of stance detection techniques
on social media, including the task definition, different types of targets in
stance detection, features set used, and various machine learning approaches
applied. The survey reports state-of-the-art results on the existing benchmark
datasets on stance detection, and discusses the most effective approaches. In
addition, this study explores the emerging trends and different applications of
stance detection on social media. The study concludes by discussing the gaps in
the current existing research and highlights the possible future directions for
stance detection on social media.Comment: We request withdrawal of this article sincerely. We will re-edit this
paper. Please withdraw this article before we finish the new versio
Pom1 regulates the assembly of Cdr2-Mid1 cortical nodes for robust spatial control of cytokinesis
Proper division plane positioning is essential to achieve faithful DNA segregation and to control daughter cell size, positioning, or fate within tissues. In Schizosaccharomyces pombe, division plane positioning is controlled positively by export of the division plane positioning factor Mid1/anillin from the nucleus and negatively by the Pom1/DYRK (dual-specificity tyrosine-regulated kinase) gradients emanating from cell tips. Pom1 restricts to the cell middle cortical cytokinetic ring precursor nodes organized by the SAD-like kinase Cdr2 and Mid1/anillin through an unknown mechanism. In this study, we show that Pom1 modulates Cdr2 association with membranes by phosphorylation of a basic region cooperating with the lipid-binding KA-1 domain. Pom1 also inhibits Cdr2 interaction with Mid1, reducing its clustering ability, possibly by down-regulation of Cdr2 kinase activity. We propose that the dual regulation exerted by Pom1 on Cdr2 prevents Cdr2 assembly into stable nodes in the cell tip region where Pom1 concentration is high, which ensures proper positioning of cytokinetic ring precursors at the cell geometrical center and robust and accurate division plane positioning
- …