Esta tese apresenta um modelo de relevância de opinião genérico e independente de domínio para usuários de Redes Sociais. O Social Opinion Relevance Model (SORM) é capaz de estimar a relevância de uma opinião com base em doze parâmetros distintos. Comparado com outros modelos, a principal característica que distingue o SORM é a sua capacidade para fornecer resultados personalizados de relevância de uma opinião, de acordo com o perfil da pessoa para a qual ela está sendo estimada. Devido à falta de corpus de relevância de opiniões capazes de testar corretamente o SORM, fez-se necessária a criação de um novo corpus chamado Social Opinion Relevance Corpus (SORC). Usando o SORC, foram realizados experimentos no domínio de jogos eletrônicos que ilustram a importância da personalização da relevância para alcançar melhores resultados, baseados em métricas típicas de Recuperação de Informação. Também foi realizado um teste de significância estatística que reforça e confirma as vantagens que o SORM oferece. / This thesis presents a generic and domain independent opinion relevance model for Social Network users. The Social Opinion Relevance Model (SORM) is able to estimate an opinions relevance based on twelve different parameters. Compared to other models, SORMs main distinction is its ability to provide customized results, according to whom the opinion relevance is being estimated for. Due to the lack of opinion relevance corpora that are able to properly test our model, we have created a new one called Social Opinion Relevance Corpus (SORC). Using SORC, we carried out some experiments on the Electronic Games domain that illustrate the importance of customizing opinion relevance in order to achieve better results, based on typical Information Retrieval metrics, such as NDCG, QMeasure and MAP. We also performed a statistical significance test that reinforces and corroborates the advantages that SORM offers.

Esta tese apresenta um modelo de relevância de opinião genérico e independente de domínio para usuários de Redes Sociais. O Social Opinion Relevance Model (SORM) é capaz de estimar a relevância de uma opinião com base em doze parâmetros distintos. Comparado com outros modelos, a principal característica que distingue o SORM é a sua capacidade para fornecer resultados personalizados de relevância de uma opinião, de acordo com o perfil da pessoa para a qual ela está sendo estimada. Devido à falta de corpus de relevância de opiniões capazes de testar corretamente o SORM, fez-se necessária a criação de um novo corpus chamado Social Opinion Relevance Corpus (SORC). Usando o SORC, foram realizados experimentos no domínio de jogos eletrônicos que ilustram a importância da personalização da relevância para alcançar melhores resultados, baseados em métricas típicas de Recuperação de Informação. Também foi realizado um teste de significância estatística que reforça e confirma as vantagens que o SORM oferece. / This thesis presents a generic and domain independent opinion relevance model for Social Network users. The Social Opinion Relevance Model (SORM) is able to estimate an opinions relevance based on twelve different parameters. Compared to other models, SORMs main distinction is its ability to provide customized results, according to whom the opinion relevance is being estimated for. Due to the lack of opinion relevance corpora that are able to properly test our model, we have created a new one called Social Opinion Relevance Corpus (SORC). Using SORC, we carried out some experiments on the Electronic Games domain that illustrate the importance of customizing opinion relevance in order to achieve better results, based on typical Information Retrieval metrics, such as NDCG, QMeasure and MAP. We also performed a statistical significance test that reinforces and corroborates the advantages that SORM offers.

Online Health Seeking (OHS) is widespread and widely studied, but its ideal fit in healthcare is still unclear. OHS is seemingly emblematic of patient self-interest and control and is an intuitive fit with the tenets of patient-centered care (PCC). Researchers have made only a few attempts to evidence or leverage this connection, focusing instead on describing the figures and typical characteristics of OHS. Finding, consuming and sharing online health and wellness information is one of the common online activities, and consumers are generally satisfied with their results despite using simple and error-prone search strategies. Physicians are interested in their patients' OHS, but for a variety of constraints including time, compensation and traditional roles in medicine, most patient OHS goes unshared with doctors. Healthcare facilitators, a relatively new class of health professional that works to bridge the gap between their client's health and personal life, may be an ideal partner for patients in OHS. In this dissertation I share my investigation of the OHS-PCC connection, presenting a case study of a type of healthcare facilitator that has embraced OHS. By studying OHS, I was also able to contribute to the collaborative information seeking (CIS) community. CIS theory and social search tools have pointed to social factors that can influence the entire process of information seeking. In this dissertation I argue that nearly any social search design can be seen as situated or embedded in a unique socio-environmental context. I suggest that social search tools can be used as probes to understand the environment, and that interactions with a search tool can illustrate phenomena far beyond direct search motivations and goals. I also hypothesize that social search field studies can produce changes in their environment, producing changes in user relationships outside of the experimental search system. My study of OHS is an opportunity to test these hypotheses by creating a collaborative search tool that seeks to use OHS as a tool to improve patient-provider relationships. In this dissertation I present the results of a series of field studies at a local clinic that centers on a unique form of health facilitator. Drawing on several formative investigations and related work I synthesize design guidelines for a collaborative OHS tool and describe Snack, a collaborative search tool for OHS customized to my field site. I also present results from Snack's field study and an analysis of email messages between advisors and clients at the clinic. My results show that these health facilitators embraced OHS as a tool to guide and connect with their clients, but fell from this practice after a change at their clinic. After analyzing these results I discuss what makes health facilitators good OHS partners and cover implications for future OHS-based interventions. I also report the positive connections I found between OHS and other quality of care indicators like patient-centered care and the Multidimensional Health Locus of Control. Finally, I consider social search's utility as a probe and intervention in light of my results.

abstract: Online social media is popular due to its real-time nature, extensive connectivity and a large user base. This motivates users to employ social media for seeking information by reaching out to their large number of social connections. Information seeking can manifest in the form of requests for personal and time-critical information or gathering perspectives on important issues. Social media platforms are not designed for resource seeking and experience large volumes of messages, leading to requests not being fulfilled satisfactorily. Designing frameworks to facilitate efficient information seeking in social media will help users to obtain appropriate assistance for their needs and help platforms to increase user satisfaction. Several challenges exist in the way of facilitating information seeking in social media. First, the characteristics affecting the user’s response time for a question are not known, making it hard to identify prompt responders. Second, the social context in which the user has asked the question has to be determined to find personalized responders. Third, users employ rhetorical requests, which are statements having the syntax of questions, and systems assisting information seeking might be hindered from focusing on genuine questions. Fouth, social media advocates of political campaigns employ nuanced strategies to prevent users from obtaining balanced perspectives on issues of public importance. Sociological and linguistic studies on user behavior while making or responding to information seeking requests provides concepts drawing from which we can address these challenges. We propose methods to estimate the response time of the user for a given question to identify prompt responders. We compute the question specific social context an asker shares with his social connections to identify personalized responders. We draw from theories of political mobilization to model the behaviors arising from the strategies of people trying to skew perspectives. We identify rhetorical questions by modeling user motivations to post them. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

Since social media gains ever increasing popularity, Online Social Networks have become important repositories for information retrieval. The concept of social search, therefore, is gradually being recognized as the next breakthrough in this field, and it is expected to dominate topics in industry. However, retrieving information from OSNs with high Quality of Experience is non-trivial as a result of the prevalence of mobile applications for social networking services. For the sake of shortening user perceived latency Web caching was introduced and has been studied extensively for years. Nevertheless, the previous works seldom focus on the Web caching solutions for social search. In the context of this master’s thesis project, emphasis is given to the design of a Web caching system which is used to cache public data from social media with the objective of improving the user experience in terms of the freshness of data and the perceived service latency. To be more specific, a Web caching strategy named Staleness Bounded LRU algorithm is proposed to limit the term of validity of the cached data. In addition, a Two-Level Web Caching System that adopts the SB-LRU algorithm is proposed in order for shortening the user perceived latency. Results of trace-driven simulations and performance evaluations demonstrate that serving clients with stale data is avoided and the user perceived latencies are significantly shortened when the proposed Web caching system is used in the use case of unauthenticated social search. Besides, the design idea in this project is believed to be helpful to the design of a Web caching system for social search, which is capable of caching user specific data for different clients.

Nous abordons dans cette thèse quelques-unes des questions soulevées par I'émergence d'applications sociales sur le Web, en se concentrant sur deux axes importants: l'efficacité de recherche sociale dans les applications Web et l'inférence de liens sociaux signés à partir des interactions entre les utilisateurs dans les applications Web collaboratives. Nous commençons par examiner la recherche sociale dans les applications de "tag- ging". Ce problème nécessite une adaptation importante des techniques existantes, qui n'utilisent pas des informations sociaux. Dans un contexte ou le réseau est importante, on peut (et on devrait) d'exploiter les liens sociaux, ce qui peut indiquer la façon dont les utilisateurs se rapportent au demandeur et combien de poids leurs actions de "tagging" devrait avoir dans le résultat. Nous proposons un algorithme qui a le potentiel d'évoluer avec la taille des applications actuelles, et on le valide par des expériences approfondies. Comme les applications de recherche sociale peut être considérée comme faisant partie d'une catégorie plus large des applications sensibles au contexte, nous étudions le problème de répondre aux requêtes à partir des vues, en se concentrant sur deux sous-problèmes importants. En premier, la manipulation des éventuelles différences de contexte entre les différents points de vue et une requête d'entrée conduit à des résultats avec des score incertains, valables pour le nouveau contexte. En conséquence, les algorithmes top-k actuels ne sont plus directement applicables et doivent être adaptés aux telle incertitudes dans les scores des objets. Deuxièmement, les techniques adaptées de sélection de vue sont nécessaires, qui peuvent s’appuyer sur les descriptions des requêtes et des statistiques sur leurs résultats. Enfin, nous présentons une approche pour déduire un réseau signé (un "réseau de confiance") à partir de contenu généré dans Wikipedia. Nous étudions les mécanismes pour deduire des relations entre les contributeurs Wikipédia - sous forme de liens dirigés signés - en fonction de leurs interactions. Notre étude met en lumière un réseau qui est capturée par l’interaction sociale. Nous examinons si ce réseau entre contributeurs Wikipedia représente en effet une configuration plausible des liens signes, par l’étude de ses propriétés globaux et locaux du reseau, et en évaluant son impact sur le classement des articles de Wikipedia. / We address in this thesis some of the issues raised by the emergence of social applications on the Web, focusing on two important directions: efficient social search inonline applications and the inference of signed social links from interactions between users in collaborative Web applications. We start by considering social search in tagging (or bookmarking) applications. This problem requires a significant departure from existing, socially agnostic techniques. In a network-aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result build-up. We propose an algorithm that has the potential to scale to current applications, and validate it via extensive experiments. As social search applications can be thought of as part of a wider class of context-aware applications, we consider context-aware query optimization based on views, focusing on two important sub-problems. First, handling the possible differences in context between the various views and an input query leads to view results having uncertain scores, i.e., score ranges valid for the new context. As a consequence, current top-k algorithms are no longer directly applicable and need to be adapted to handle such uncertainty in object scores. Second, adapted view selection techniques are needed, which can leverage both the descriptions of queries and statistics over their results. Finally, we present an approach for inferring a signed network (a "web of trust")from user-generated content in Wikipedia. We investigate mechanisms by which relationships between Wikipedia contributors - in the form of signed directed links - can be inferred based their interactions. Our study sheds light into principles underlying a signed network that is captured by social interaction. We investigate whether this network over Wikipedia contributors represents indeed a plausible configuration of link signs, by studying its global and local network properties, and at an application level, by assessing its impact in the classification of Wikipedia articles.javascript:nouvelleZone('abstract');_ajtAbstract('abstract');

The main contributions of this thesis revolve around development of an integrated conversational recommendation system, combining data and information models with community network and interactions to leverage multi-modal information access. We have developed a real time conversational information access community agent that leverages community knowledge by pushing relevant recommendations to users of the community. The recommendations are delivered in the form of web resources, past conversation and people to connect to. The information agent (cobot, for community/ collaborative bot) monitors the community conversations, and is 'aware' of users' preferences by implicitly capturing their short term and long term knowledge models from conversations. The agent leverages from health and medical domain knowledge to extract concepts, associations and relationships between concepts; formulates queries for semantic search and provides socio-semantic recommendations in the conversation after applying various relevance filters to the candidate results. The agent also takes into account users' verbal intentions in conversations while making recommendation decision. One of the goals of this thesis is to develop an innovative approach to delivering relevant information using a combination of social networking, information aggregation, semantic search and recommendation techniques. The idea is to facilitate timely and relevant social information access by mixing past community specific conversational knowledge and web information access to recommend and connect users with relevant information. Language and interaction creates usable memories, useful for making decisions about what actions to take and what information to retain. Cobot leverages these interactions to maintain users' episodic and long term semantic models. The agent analyzes these memory structures to match and recommend users in conversations by matching with the contextual information need. The social feedback on the recommendations is registered in the system for the algorithms to promote community preferred, contextually relevant resources. The nodes of the semantic memory are frequent concepts extracted from user's interactions. The concepts are connected with associations that develop when concepts co-occur frequently. Over a period of time when the user participates in more interactions, new concepts are added to the semantic memory. Different conversational facets are matched with episodic memories and a spreading activation search on the semantic net is performed for generating the top candidate user recommendations for the conversation. The tying themes in this thesis revolve around informational and social aspects of a unified information access architecture that integrates semantic extraction and indexing with user modeling and recommendations.

Cette recherche-action menée au sein de la DSIC de La Poste s’intéresse à la réputation des organisations et à son pendant numérique (l’e-réputation) par le prisme des sciences de l’information-communication. Elle propose le développement d’un processus et d’un dispositif de veille stratégique par infomédiation sociale permettant à une organisation d’évaluer, de gérer et in fine de construire son e-réputation.Dans un premier temps, nous présentons un cadre théorique de la réputation des organisations comme objet info-communicationnel. Puis nous abordons l’e-réputation (ou réputation en ligne) des organisations comme une information stratégique constituée de l’ensemble des indicateurs issus de la commensuration des interactions intentionnelles endogènes ou automatisées des communautés virtuelles avec l’organisation : productions d’opinions, notations ou encore agir des publics.Dans un deuxième temps, nous nous questionnons sur les moyens à disposition du groupe La Poste pour construire cette e-réputation. Par « construction », nous entendons la manière de structurer l’environnement informationnel dans lequel les publics de l’organisation évoluent chaque jour sur le web dit social, et plus spécifiquement sur la plate-forme Twitter. Dans ce cadre, nos observations empiriques nous permettent de mettre en exergue, de questionner et d’analyser des utilisateurs du web dont les pratiques informationnelles nous amènent à les qualifier « d’agents-facilitateurs », et dont la caractéristique principale est de participer à la prescription informationnelle et à la médiation documentaire sur le web.Suite à l’observation de certains de ces agents et à la production d’une typologie, nous proposons un modèle et un processus de veille les intégrant. Processus dont les résultats opérationnels au sein de La Poste mettent en avant la nécessaire prise en compte et analyse des pratiques de recommandation des internautes au sein des réseaux socionumériques afin que l’organisation construise son e-réputation de manière proactive. / This research within La Poste's DSIC is focused on corporate reputation and on e-reputation through the prism of information and communication sciences. It proposes the development of a web monitoring and a social search process enabling to assess, manage and build online reputation.At first, we present a theoretical framework for corporate reputation as an info-communicational object. Then we discuss corporate e-reputation such as a strategic information made up of all commensuration indicators constituted by intentional (endogenous or not) interactions with the virtual communities and the firm : productions of opinions, assessment or public behavior.In a second step, we investigate how La Poste group can build its e-reputation. By "build" we mean the way to structure the informational environment where customers evolve each day on social web, and more specifically on the Twitter platform. In this context, our empirical observations allow us to highlight, question and analyze, web users whose informational practices permit us to qualify of "facilitators-agents", and whose main characteristic is to participate in informational prescription and the documentary mediation on the web.After the observation of some of these agents and the production of a typology, we propose a model and a monitoring process to integrate them. Our process's results in La Poste show the need to take into account and analyse recommendation practices on social networks to build corporate e-reputation proactively.