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Background Information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. Health information could be transmitted from one to many (i.e. broadcasting) or from a chain of individual to individual (i.e. viral spreading). The aim of this study is to examine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. Methods Our data was purchased from GNIP. We obtained all Ebola-related tweets posted globally from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships. Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. Results On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcasting was more pervasive than viral spreading. We found that influential users and hidden influential users triggered more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. Conclusions Broadcasting was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work beneficially with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger many retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion. However, challenges remain due to uncertain credibility of these hidden influential users.

Technology transfer is an innovation process far from to be defined as a simple transmitter / receiver relationship of knowledge. It is complex. Thus the determinants of its success are still poorly understood and its modeling remains to be studied to a better management and optimization of the process.This thesis proposes a descriptive modeling of the technology transfer process. The aim is to have better understanding of the dynamics of technology transfer projects, and developing best practices to improve its management.In the theoretical field, we analyzed the models of the literature and proposed a meta-model of technology transfer from the point of view of systems engineering. We then sought to better understand the phenomena in situ.In order to reach our aim, an observation methodology for data collection at the micro level has been developed. We followed five transfer projects for a period ranging from three months to two years. Two dimensions have been emphasized: the immaterial and the material dimension. The concept of Intermediate Transfer Object (ITO) is introduced from the concept of design intermediary object.The data obtained were analyzed using two approaches:- a comparative descriptive approach, identifying invariants and divergent phenomena between the five processes. This has allowed us to propose best practices for technology transfer project management in the context of brazing.- a multicriteria approach based on the rough sets theory. This approach provides useful information for understanding the process through the decision rules. It validated the importance of the technology transfer object in the dynamics and the success of a project; Le transfert de technologie est un processus d'innovation loin de se résumer à une simple relation émetteur / récepteur de connaissances. Il est complexe et de ce fait, les facteurs déterminants de son succès sont encore mal connus, sa modélisation reste à étudier et des principes de pilotage sont à établir.Cette thèse propose une modélisation descriptive du processus de transfert de technologie afin de mieux comprendre la dynamique des projets de transfert de technologie et de dégager des bonnes pratiques permettant de mieux le piloter. Dans le champ théorique, nous avons analysé les modèles de transfert de technologie existant dans la littérature et avons proposé un méta-modèle du point de vue de l'ingénierie système. Nous avons ensuite cherché à mieux comprendre les phénomènes in situ.Pour ce faire, une méthodologie d'observation pour la collecte des données au niveau « micro » a été mise au point. Nous avons suivi cinq projets de transfert durant une période allant de trois mois à deux ans. Deux dimensions ont été privilégiées : la dimension immatérielle et matérielle. Le concept d'Objet Intermédiaire de Transfert (OIT) est introduit à partir de la notion d'Objet Intermédiaire de Conception. Les données obtenues ont été analysées selon deux approches :- une approche comparative descriptive, permettant d'identifier les invariants et les phénomènes divergents entre les cinq processus. - une approche multicritère basée sur la théorie des ensembles approximatifs. Cette dernière approche fournit des informations utiles pour la compréhension du processus par l'intermédiaire des règles de connaissances. Elle a validé l'importance des OIT dans la dynamique du projet final

Technology transfer is an innovation process far from to be defined as a simple transmitter / receiver relationship of knowledge. It is complex. Thus the determinants of its success are still poorly understood and its modeling remains to be studied to a better management and optimization of the process.This thesis proposes a descriptive modeling of the technology transfer process. The aim is to have better understanding of the dynamics of technology transfer projects, and developing best practices to improve its management.In the theoretical field, we analyzed the models of the literature and proposed a meta-model of technology transfer from the point of view of systems engineering. We then sought to better understand the phenomena in situ.In order to reach our aim, an observation methodology for data collection at the micro level has been developed. We followed five transfer projects for a period ranging from three months to two years. Two dimensions have been emphasized: the immaterial and the material dimension. The concept of Intermediate Transfer Object (ITO) is introduced from the concept of design intermediary object.The data obtained were analyzed using two approaches:- a comparative descriptive approach, identifying invariants and divergent phenomena between the five processes. This has allowed us to propose best practices for technology transfer project management in the context of brazing.- a multicriteria approach based on the rough sets theory. This approach provides useful information for understanding the process through the decision rules. It validated the importance of the technology transfer object in the dynamics and the success of a project, Le transfert de technologie est un processus d'innovation loin de se résumer à une simple relation émetteur / récepteur de connaissances. Il est complexe et de ce fait, les facteurs déterminants de son succès sont encore mal connus, sa modélisation reste à étudier et des principes de pilotage sont à établir.Cette thèse propose une modélisation descriptive du processus de transfert de technologie afin de mieux comprendre la dynamique des projets de transfert de technologie et de dégager des bonnes pratiques permettant de mieux le piloter. Dans le champ théorique, nous avons analysé les modèles de transfert de technologie existant dans la littérature et avons proposé un méta-modèle du point de vue de l'ingénierie système. Nous avons ensuite cherché à mieux comprendre les phénomènes in situ.Pour ce faire, une méthodologie d'observation pour la collecte des données au niveau « micro » a été mise au point. Nous avons suivi cinq projets de transfert durant une période allant de trois mois à deux ans. Deux dimensions ont été privilégiées : la dimension immatérielle et matérielle. Le concept d'Objet Intermédiaire de Transfert (OIT) est introduit à partir de la notion d'Objet Intermédiaire de Conception. Les données obtenues ont été analysées selon deux approches :- une approche comparative descriptive, permettant d'identifier les invariants et les phénomènes divergents entre les cinq processus. - une approche multicritère basée sur la théorie des ensembles approximatifs. Cette dernière approche fournit des informations utiles pour la compréhension du processus par l'intermédiaire des règles de connaissances. Elle a validé l'importance des OIT dans la dynamique du projet final