Dissémination de l’information et dynamique des opinions dans les réseaux sociaux

Our aim in this Ph. D. thesis is to study the diffusion of information as well as the opinion dynamics of users in social networks. Information diffusion models explore the paths taken by information being transmitted through a social network in order to understand and analyze the relationships between users in such network, leading to a better comprehension of human relations and dynamics. This thesis is based on both sides of information diffusion: first by developing mathematical theories and models to study the relationships between people and information, and in a second time by creating tools to better exploit the hidden patterns in these relationships. The theoretical tools developed in this thesis are opinion dynamics models and information diffusion models, where we study the information flow from users in social networks, and the practical tools developed in this thesis are a novel community detection algorithm and a novel trend detection algorithm. We start by introducing an opinion dynamics model in which agents interact with each other about several distinct opinions/contents. In our framework, agents do not exchange all their opinions with each other, they communicate about randomly chosen opinions at each time. We show, using stochastic approximation algorithms, that under mild assumptions this opinion dynamics algorithm converges as time increases, whose behavior is ruled by how users choose the opinions to broadcast at each time. We develop next a community detection algorithm which is a direct application of this opinion dynamics model: when agents broadcast the content they appreciate the most. Communities are thus formed, where they are defined as groups of users that appreciate mostly the same content. This algorithm, which is distributed by nature, has the remarkable property that the discovered communities can be studied from a solid mathematical standpoint. In addition to the theoretical advantage over heuristic community detection methods, the presented algorithm is able to accommodate weighted networks, parametric and nonparametric versions, with the discovery of overlapping communities a byproduct with no mathematical overhead. In a second part, we define a general framework to model information diffusion in social networks. The proposed framework takes into consideration not only the hidden interactions between users, but as well the interactions between contents and multiple social networks. It also accommodates dynamic networks and various temporal effects of the diffusion. This framework can be combined with topic modeling, for which several estimation techniques are derived, which are based on nonnegative tensor factorization techniques. Together with a dimensionality reduction argument, this techniques discover, in addition, the latent community structure of the users in the social networks. At last, we use one instance of the previous framework to develop a trend detection algorithm designed to find trendy topics in a social network. We take into consideration the interaction between users and topics, we formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the distance between the real broadcast intensity and the maximum expected broadcast intensity and the social network topology. The proposed trend detection algorithm uses stochastic control techniques in order calculate the trend indices, is fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of necessary data to the detection. To the best of our knowledge, this is the first trend detection algorithm that is based solely on the individual performances of topicsLa dissémination d'information explore les chemins pris par l'information qui est transmise dans un réseau social, afin de comprendre et modéliser les relations entre les utilisateurs de ce réseau, ce qui permet une meilleur compréhension des relations humaines et leurs dynamique. Même si la priorité de ce travail soit théorique, en envisageant des aspects psychologiques et sociologiques des réseaux sociaux, les modèles de dissémination d'information sont aussi à la base de plusieurs applications concrètes, comme la maximisation d'influence, la prédication de liens, la découverte des noeuds influents, la détection des communautés, la détection des tendances, etc. Cette thèse est donc basée sur ces deux facettes de la dissémination d'information: nous développons d'abord des cadres théoriques mathématiquement solides pour étudier les relations entre les personnes et l'information, et dans un deuxième moment nous créons des outils responsables pour une exploration plus cohérente des liens cachés dans ces relations. Les outils théoriques développés ici sont les modèles de dynamique d'opinions et de dissémination d'information, où nous étudions le flot d'informations des utilisateurs dans les réseaux sociaux, et les outils pratiques développés ici sont un nouveau algorithme de détection de communautés et un nouveau algorithme de détection de tendances dans les réseaux sociau

Trend detection in social networks using Hawkes processes

International audienceWe develop in this paper a trend detection algorithm , designed to find trendy topics being disseminated in a social network. We assume that the broadcasts of messages in the social network is governed by a self-exciting point process, namely a Hawkes process, which takes into consideration the real broadcasting times of messages and the interaction between users and topics. We formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the time between the detection and the message broadcasts, the distance between the real broadcast intensity and the maximum expected broadcast intensity, and the social network topology. The proposed trend detection algorithm is simple and uses stochastic control techniques in order to calculate the trend indices. It is also fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of data necessary to the detection

A framework for information dissemination in social networks using Hawkes processes

International audienceWe define in this paper a general Hawkes-based framework to model information diffusion in social networks. The proposed framework takes into consideration the hidden interactions between users as well as the interactions between contents and social networks, and can also accommodate dynamic social networks and various temporal effects of the diffusion, which provides a complete analysis of the hidden influences in social networks. This framework can be combined with topic modeling, for which modified collapsed Gibbs sampling and variational Bayes techniques are derived. We provide an estimation algorithm based on nonnegative tensor factorization techniques, which together with a dimensionality reduction argument are able to discover , in addition, the latent community structure of the social network. At last, we provide numerical examples from real-life networks: a Game of Thrones and a MemeTracker datasets

Dissemination and competition between contents in lossy Susceptible Infected Susceptible (SIS) social networks

International audienceWe model in this work dissemination of contents and competition between sources of contents in social networks composed of a given number of resources (channels or links) used by sources for dissemination of their contents, in the case where some of these resources may be lost during the propagation process, corresponding to the so-called Susceptible Infected Susceptible (SIS) model. We consider two approaches: a static one wherein the source can apply some control, in terms of advertisement for instance, at the start of the dissemination process, and a dynamic one where control can be done at any point in time. We derive, in each case, optimal controls and strategies that maximize the distribution of the contents for linear cost functions, and characterize the Nash equilibrium of corresponding games: static game for the static optimization case, and differential and stochastic games for the dynamic case, the former when the information on content dissemination is not available at the sources, and the latter when it i

Incidência de tuberculose em pacientes com artrite reumatoide em uso de bloqueadores do TNF no Brasil: dados do Registro Brasileiro de Monitoração de Terapias Biológicas BiobadaBrasil

Objectives: To assess the incidence of tuberculosis and to screen for latent tuberculosis infection among Brazilians with rheumatoid arthritis using biologics in clinical practice. Patients and methods: This cohort study used data from the Brazilian Registry of Biological Therapies in Rheumatic Diseases (Registro Brasileiro de Monitoração de Terapias Biológicas - BiobadaBrasil), from 01/2009 to 05/2013, encompassing 1552 treatments, including 415 with only synthetic disease-modifying anti-rheumatic drugs, 942 synthetic DMARDs combined with anti-tumor necrosis factor (etanercept, infliximab, adalimumab) and 195 synthetic DMARDs combined with other biologics (abatacept, rituximab and tocilizumab). The occurrence of tuberculosis and the drug exposure time were assessed, and screening for tuberculosis was performed. Statistical analysis: Unpaired t-test and Fisher's two-tailed test; p < 0.05. Results: The exposure times were 981 patient-years in the controls, 1744 patient-years in the anti-TNF group (adalimumab = 676, infliximab = 547 and etanercept = 521 patient-years) and 336 patient-years in the other biologics group. The incidence rates of tuberculosis were 1.01/1000 patient-years in the controls and 2.87 patient-years among anti-TNF users (adalimumab = 4.43/1000 patient-years; etanercept = 1.92/1000 patient-years and infliximab = 1.82/1000 patient-years). No cases of tuberculosis occurred in the other biologics group. The mean drug exposure time until the occurrence of tuberculosis was 27(11) months for the anti-TNF group. Conclusions: The incidence of tuberculosis was higher among users of synthetic DMARDs and anti-TNF than among users of synthetic DMARDs and synthetic DMARDs and non-anti-TNF biologics and also occurred later, suggesting infection during treatment and no screening failure.Objetivos: Avaliar incidência de tuberculose e triagem para tuberculose latente em brasileiros com artrite reumatoide em uso de agentes biológicos na prática clinica. Pacientes e métodos: Estudo de coorte com dados do Registro Brasileiro de Monitoração de Terapias Biológicas (BiobadaBrasil), de 01/2009 a 05/2013, abrangeu 1.552 tratamentos, 415 somente com drogas modificadoras do curso da doença (MMCDs) sintéticas, 942 MMCDs sintéticas em associação com anti-TNF (etanercepte, infliximabe, adalimumabe) e 195 MMCDs sintéticas em associação com outros biológicos (abatacepte, rituximabe e tocilizumabe). Avaliaram-se ocorrência de tuberculose, tempo de exposição às drogas e triagem para TB. Análise estatística: teste t não pareado e teste de Fisher bicaudal; p < 0,05. Resultados: O tempo de exposição dos controles foi de 981 pacientes-ano, do grupo de anti-TNF foi de 1.744 pacientes-ano (adalimumabe = 676, infliximabe = 547 e etanercepte = 521 pacientes-ano) e o de outros biológicos de 336 pacientes-ano. A incidência de TB foi de 1,01/1.000 pacientes-ano nos controles e de 2,87 pacientes-ano nos usuários de anti-TNF (adalimumabe = 4,43/1.000 pacientes-ano; etanercepte = 1,92/1.000 pacientes-ano e infliximabe = 1,82/1.000 pacientes-ano). Não houve casos de tuberculose no grupo de outros biológicos. O tempo médio de exposição até a ocorrência de tuberculose foi de 27(11) meses para o grupo anti-TNF. Conclusões: A incidência de tuberculose foi maior nos usuários de MMCDs sintéticas e anti-TNF do que nos usuários de MMCDs sintéticas e de MMCDs sintéticas e biológicos não anti-TNF, e também mais tardia, sugerindo infecção durante o tratamento, e não falha na triagem

Changing rate of serious infections in biologic-exposed rheumatoid arthritis patients : data from South American registries BIOBADABRASIL and BIOBADASAR

Most reports on serious infections (SI) in rheumatoid arthritis (RA) patients treated with biological disease-modifying antirheumatic drugs (bDMARDs) are from the USA and Western Europe. Data from other regions are largely missing. We report data from South American countries with different backgrounds and health-care systems but similar registries. We merged 2010-2016 data from two registries, BIOBADABRASIL (Brazil) and BIOBADASAR (Argentina), which share the same protocol, online platform and data monitoring process. Patients with active RA were included when they began the first bDMARD or a conventional synthetic DMARD (csDMARD, control group). The SI incidence rate (IR) per 1000 patient/years and adjusted IR ratio (aIRR) were estimated for bDMARDs and csDMARDs. Data were analysed for 3717 RA patients with an exposure of 13,380 patient/years. The 2591 patients treated with bDMARDs (64% tumour necrosis factor-alpha inhibitors (TNFi)) had a follow-up of 9300years, and the 1126 treated with csDMARDs had an exposure of 4081 patient/years. The SI IR was 30.54 (CI 27.18-34.30) for all bDMARDs and 5.15 (CI 3.36-7.89) for csDMARDs. The aIRR between the two groups was 2.03 ([1.05, 3.9] p=0.034) for the first 6months of treatment but subsequently increased to 8.26 ([4.32, 15.76] p<0.001). The SI IR for bDMARDs decreased over time in both registries, dropping from 36.59 (28.41-47.12) in 2012 to 7.27 (4.79-11.05) in 2016. While SI remains a major concern in South American patients with RA treated with bDMARDs, a favourable trend toward a reduction was observed in the last years3882129213