Martine Collard’s research while affiliated with University of the French Antilles and other places

Climate information plays a foundational role in achieving a green recovery and climate neutrality in Europe, and a central one for a climate resilient Europe. This role can materialize if climate information is delivered appropriately and used effectively. Climate services, understood as the provision of climate information for use in decision making, have been created to provide climate information addressing these aspects. The utility of climate services is determined by the level of user engagement and co-design, employed during their development, while resource limitations for any of these aspects constrain their full potential. Co-design together with users is increasingly seen as a necessary good practice approach to provide efficient services that bring together supply and demand. In this paper, we focus on how climate services can contribute to climate neutrality by considering the cultural dimension of the users and their regions for whom specific climate services are co-designed. We specifically address dimensions of vulnerability and resilience to changing climatic conditions in five case studies worldwide while analysing the influence of culture on risk coping and enabling mechanism of key stakeholders and their needs for specific climate services in these regions. We found that user needs, desires and actions hinge on value prepositions formed by specific socio-cultural, climatic, spatial and bio-ecological contexts. Hence, when co-designing climate services, it is vital to understand users’ needs, based on their values and experiences with climate and weather and to seek ways to influence, alter and change them.

Climate services were initially established with the aim to make the vast amount of climate data, projections and other climate science output publicly available to support the development of responses to society’s vulnerability to climate change. In Europe embraced the concept was not only embraced to provide access to scientific knowledge and reduce vulnerability, but also as an opportunity to promote innovation, business opportunities and employment, highlighting the importance of involving users in developing climate services. However, not only differences in knowledge and skills, but also in framing of climate risks and information needs, pose a serious gap between suppliers and users of climate information, sometimes called the “valley of death”. Focusing on urban and rural development at the regional and local level as key areas of application for climate services, the paper characterizes this valley of death and suggests options to bridge the gap. We suggest that reframing of the concept of climate services can help expand their applications and effectiveness, taking local non-climate challenges, opportunities and narratives into account. We provide examples from the European ERA4CS project INNOVA. The current focus of climate service development is very much on digital forms of climate change information. While this may provide a useful “back office” function, active brokerage and mediated transfer of knowledge between public and private actors, face-to-face collaboration between providers and clients (“front office”), and integration of social, economic and non-climate environmental challenges with climate risks can help bridging the “valley of death”.

This article falls within the related areas of climate services and geographic information. We present the architecture and features of the Data and Knowledge Platform (DKP), innovative geographic software that was designed as support for climate-service elaboration in the context of change on given geographic areas. It is intended for a community of stakeholders who need visual and geographic tools to design services improving the resilience of society regarding specific local issues. The platform provides different functions for seeking all available geographic information. Anticipating large volumes of data that are to be stored, we opted for a NoSQL database rather than a textual repository. In this paper, we present the different features of the platform and its ability to support visual climate service co-design, and we illustrate our statement with an example.

The study of information dissemination in social networks is of particular importance in many areas as marketing, politics and security for example. Various strategies are being developed to disseminate information, those aimed at disseminating information widely and those aimed at disseminating information in a more confidential manner to make it scarce. In this paper, we adapt a model dedicated to spreading rumours by word of mouth in a physical space to the context of social networks. We compare two modes of dissemination based on profusion or scarcity and study the impact of the choice of the initial node. The results obtained show to what extent each mode exploits the social network topology and especially the influence of hubs.

Today, social media are one of the fastest ways to have access to information related to several topics. Indeed, a diffused information on these supports can travel thousands of kilometres in only few seconds contrary to an article posted on a news site. Despite the fact that a large variety of studies have been conducted to understand how fast and how scale information spreads in social media, we observe that they have not yet been interested in the geographical aspect. In this paper, we perform a geographical and temporal analysis of Twitter trends spread between May and June 2017. We introduce interesting patterns which deal with the paths taken by information between countries. In addition, we observe relevant results by taking into account the topic. Finally, we conclude and give perspectives of research of this work.

Nowadays, social networks are one of the most used means of communication. For example, the social network Twitter has nearly 100 million active users who post about 500 million messages per day. Sharing information on this platform is unique because messages are limited in characters number. Faced with this limitation, users express themselves briefly and use sometimes a hashtag that summarizes the general idea of the message. Nevertheless, hashtags are noisy data because they do not respect any linguistic rule, may have several meanings, and their use is not under control. In this work, we tackle the problem of hashtag context which may have useful applications in several fields like information recommendation or information classification. In this paper, we propose an original data cleaning method to extract the most relevant neighbor hashtags of a hashtag. We test our method with a dataset containing hashtags related to several topics (such as sport, music, technology, etc.) in order to show the efficacy and the robustness of our approach.

Social networks are used on a daily basis by millions of individuals who post millions of messages on several topics. The data recorded by these networks provide useful information in order to predict or detect events in the real world. Some diffused messages are misinformation or false rumours, and so, can be the cause of panic or stress situations. In order to avoid and anticipate these critical situations and understand the diffusion phenomenon in general, it is necessary to study and model the propagation of the information. In this sense, several varieties of models have been proposed and some researchers have attempted to identify parameters involved in the information diffusion. In this paper, we introduce well-known diffusion models that generally simplify drastically the process and we present also a survey of more advanced works whether recent or not studying factors that influence the information diffusion. Finally, we give essential perspectives of research toward a more realistic coverage of information diffusion phenomena.

The extraction of knowledge from social networks is an area that has experienced significant growth in recent years. Indeed, thanks to the improvement of storage and calculation capacities, and the heterogeneity of data that can currently be extracted, much effort has been made to go beyond traditional knowledge, by proposing new kinds of patterns that take into account the context. However, while many works were interested in designing new patterns of knowledge or in optimizing existing approaches, few studies have been focused in merging patterns and on the useful knowledge emerging from such fusions. In this work, we focus on two network clustering approaches, able to extract two distinct kinds of patterns, and we seek to understand both the intersections that can exist between them and the knowledge that emerges from their fusion. The first is the classical nodes clustering approach that consists in searching for communities into a network. The second is the search for frequent conceptual links, a new link clustering approach that aims identifying frequent links between groups of nodes sharing common attributes. We propose a set of original measures that aim to evaluate the amount of shared information between these patterns when they are extracted from a same network. These measures are applied to three datasets and demonstrate the interest in simultaneously considering several sources of knowledge.

Numerous methods have been proposed in order to perform clustering from social networks. While significant works have been carried out on the design of new approaches, able to search for various kinds of clusters, a major challenge concerns the scalability of these approaches. Indeed, given the mass of data that can now be collected from online social networks, particularly from social platforms, it is important to have efficient methods for exploring and analyzing these very large amount of data. One of the recent social network clustering approaches is the extraction of conceptual links, a new approach that performs link clustering by exploiting both the structure of the network and attributes of nodes to identify strong links between groups of nodes in which nodes share common attributes. In this paper, we focus on the optimization of the search for conceptual links. In particular, we propose PALM, a parallel algorithm that aims to improve the efficiency of the extraction by simultaneously exploring several areas of the search space. For this purpose, we begin by demonstrating that the solution space forms a concept lattice. Then, we propose an approach that explores in parallel the branches of the lattice while reducing the search space based on various properties of conceptual links. We demonstrate the efficiency of the algorithm by comparing the performances with the original extraction approach. The results obtained show a significant gain on the computation time.

In this article, we analyse a collaborative network to understand the underlying patterns that structure the co-writing process of scientific articles. Our goal is to identify and understand the collaboration tendencies from authors publishing activities. For this purpose, we adopt a descriptive modelling through a network approach that consists first in generating the collaborative network from data on publications. Nodes of the network are then enriched with a set of individual attributes extracted from the publishing activity of each author. Finally, we search for conceptual views, a recent link clustering approach, which allows to summarize any kind of networks by highlighting the sets of attributes found frequently linked. Results show that it exists strong tendencies that unconsciously structure the collaboration behaviours. In this paper, we present these tendencies and show how they evolve according to different extraction thresholds.