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如何透過視覺化探索勢力消長情形，是近年來頻繁被探討的問題，常見之做法會針對帶有時間屬性的時間關聯資料 (time-oriented data)來進行觀察，而以社群媒體為例，重大議題通常是透過意見領袖提出具有關鍵性之觀點，而得以分歧出新議題並吸引其他社群媒體上之閱聽人加入討論，上述之過程牽涉評論之階層資料其層次隨著時間變化分歧與合併，然而，能夠透過視覺化之方式同時觀察上述特性有其挑戰性。本篇論文將針對階層式資料提出一套整合方式，稱為TopicWave，特別是帶有時間變化屬性的資料，希望透過改良動態圖形視覺化工具，結合 Sunburst 與 ThemeRiver Graph，實作 Facebook 上公開文章之評論(comments)行為隨時間變化的趨勢，而透過直覺式互動功能之設計。透過案例分析和使用者測試，本論文提出的方法能清楚呈現評論關係隨時間之變化與階層式結構，達到組合式創新之效果。 / In recent research, it is a frequently asked question about how to explore the topic trend during a time interval. If we want to analysis and discuss this question, time-oriented data will be the most appropriate dataset. For example, on social media platform, major issues are commonly formed by opinion leaders, people will be attracted by opinion leaders and join in the commentary on a topic. The above-mentioned procedure will involve in commentary hierarchy level increasing or decreasing while time changes, however, it is challenging when we want to explore these properties using traditional visualization techniques. We propose TopicWave, a visualization design that combines ThemeRiver Graph (time-oriented visualization) and Sunburst (hierarchical data visualization). It can visualize the trend of a post’s comment on Facebook Page. TopicWave can clearly present hierarchy and time-varying trend of a Facebook post’s comment data at the same time through the intuitive design of interactive on visualization.

本篇論文針對社群使用者在社群網路文章上的互動行為進行研究，以視覺化工具Shareflow探索互動過程中造成資訊擴散的意見領袖以及傳播路徑。本研究主要分成兩個部分，第一部分為單一篇文章的分享路徑視覺化，基於階層化邊線綑綁(Hierarchical edge bundles)方法，根據控制點的引導將鄰近邊線進行綑綁，透過邊線捆綁舒緩資料量過大時造成之視覺混亂(visual clutter)問題。第二部分為粉絲專頁文章視覺化，分析多篇文章中具有多次分享、留言行為之使用者，呈現整體社群中積極活動的使用者以及其相關文章的視覺化。最後提供即時互動操作介面，以並列方式呈現出資料的廣度和深度，本研究的貢獻為提供一套視覺化工具，協助使用者探索臉書社群網路中的資訊散播過程以及發掘積極活動的臉書使用者。 / In this thesis, we propose a visualization tool, “Shareflow”, for observing the user activities in social media posts, and to explore opinion leaders and the propagation path caused by the information diffusion. Our approach contains two parts. The first part is a visualization of propagation path for a post in Facebook fanpage. Based on hierarchical edge bundles, we optimize the layout to reduce visual clutter caused by excessive information. The second part is visualization for a summary of posts in Facebook fanpage. It provides a tool for analysis the active users through their sharing and comments activities; In addition, we provide a real-time interactive interface, which demonstrates the breadth and depth of information concurrently.

Un certain nombre de systèmes dans le monde réel, comprenant des agents interagissant, peut être utilement modélisé par des graphes, où les agents sont représentés par les sommets du graphe et les interactions par les arêtes. De tels systèmes peuvent être aussi divers et complexes que les réseaux sociaux (traditionnels ou virtuels), les réseaux d'interaction protéine-protéine, internet, réseaux de transport et les réseaux de prêts interbancaires. Une question importante qui se pose dans l'étude de ces réseaux est: dans quelle mesure, les statistiques locales d'un réseau déterminent sa topologie globale. Ce problème peut être approché par la construction d'un graphe aléatoire contraint d'avoir les mêmes statistiques locales que celles observées dans le graphe d'intérêt. Le modèle de configuration est un tel modèle de graphe aléatoire conçu de telle sorte qu'un sommet uniformément choisi présente une distribution de degré donnée. Il fournit le cadre sous-jacent à cette thèse. En premier lieu nous considérons un problème de propagation de l'influence sur le modèle de configuration, où chaque sommet peut être influencé par l'un de ses voisins, mais à son tour, il ne peut influencer qu'un sous-ensemble aléatoire de ses voisins. Notre modèle étendu est décrit par le degré total du sommet typique et le nombre de voisins il est capable d'influencer. Nous donnons une condition stricte sur la distribution conjointe de ces deux degrés, qui permet à l'influence de parvenir, avec une forte probabilité, à un ensemble non négligeable de sommets, essentiellement unique, appelé la composante géante influencée, à condition que le sommet de la source soit choisi à partir d'un ensemble de bons pionniers. Nous évaluons explicitement la taille relative asymptotique de la composant géante influencée, ainsi que de l'ensemble des bons pionniers, à condition qu'ils soient non-négligeable. Notre preuve utilise l'exploration conjointe du modèle de configuration et de la propagation de l'influence jusqu'au moment où une grande partie est influencée, une technique introduite dans Janson et Luczak (2008). Notre modèle peut être vu comme une généralisation de la percolation classique par arêtes ou par sites sur le modèle de configuration, avec la différence résultant de la conductivité orientée des arêtes dans notre modèle. Nous illustrons ces résultats en utilisant quelques exemples, en particulier, motivés par le marketing viral - un phénomène connu dans le contexte des réseaux sociaux… / A number of real-world systems consisting of interacting agents can be usefully modelled by graphs, where the agents are represented by the vertices of the graph and the interactions by the edges. Such systems can be as diverse and complex as social networks (traditional or online), protein-protein interaction networks, internet, transport network and inter-bank loan networks. One important question that arises in the study of these networks is: to what extent, the local statistics of a network determine its global topology. This problem can be approached by constructing a random graph constrained to have some of the same local statistics as those observed in the graph of interest. One such random graph model is configuration model, which is constructed in such a way that a uniformly chosen vertex has a given degree distribution. This is the random graph which provides the underlying framework for this thesis. As our first problem, we consider propagation of influence on configuration model, where each vertex can be influenced by any of its neighbours but in its turn, it can only influence a random subset of its neighbours. Our (enhanced) model is described by the total degree of the typical vertex and the number of neighbours it is able to influence. We give a tight condition, involving the joint distribution of these two degrees, which allows with high probability the influence to reach an essentially unique non-negligible set of the vertices, called a big influenced component, provided that the source vertex is chosen from a set of good pioneers. We explicitly evaluate the asymptotic relative size of the influenced component as well as of the set of good pioneers, provided it is non-negligible. Our proof uses the joint exploration of the configuration model and the propagation of the influence up to the time when a big influenced component is completed, a technique introduced in Janson and Luczak (2008). Our model can be seen as a generalization of the classical Bond and Node percolation on configuration model, with the difference stemming from the oriented conductivity of edges in our model. We illustrate these results using a few examples which are interesting from either theoretical or real-world perspective. The examples are, in particular, motivated by the viral marketing phenomenon in the context of social networks...

Dans cette thèse nous avons étudié la diffusion de l'information dans les grands graphes de terrain, en se focalisant sur les patterns structurels de la propagation. Sur le plan empirique, il s'est avéré difficile de capturer la structure des cascades de diffusion en termes de mesures simples. Sur le plan théorique, l'approche classique consiste à étudier des modèles stochastiques de contagion. Néanmoins, l'analyse formelle de ces modèles reste limité, car les graphes de terrain ont généralement une topologie complexe et le processus de diffusion se produit dans une fenêtre de temps limitée. Par conséquent, une meilleure compréhension des données empiriques, des modèles théoriques et du lien entre les deux est également cruciale pour la caractérisation de la diffusion dans les grands graphes de terrain. Après un état de l'art sur les graphes de terrain et la diffusion dans ce contexte au premier chapitre, nous décrivons notre jeu de données et discutons sa pertinence au chapitre 2. Ensuite, dans le chapitre 3, nous évaluons la pertinence du modèle SIR simple et de deux extensions qui prennent en compte des hétérogénéités de notre jeu de données. Dans le chapitre 4, nous explorons la prise en compte du temps dans l'évolution du réseau sous-jacent et dans le modèle de diffusion. Dans le chapitre 5, nous évaluons l'impacte de la structure du graphe sous-jacent sur la structure des cascades de diffusion générées avec les modèles étudiés dans les chapitres précédents. Nous terminons la thèse par un bilan des résultats et des perspectives ouvertes par les travaux menés dans cette thèse. / Understanding information diffusion on complex networks is a key issue from a theoretical and applied perspective. Epidemiology-inspired SIR models have been proposed to model information diffusion. Recent papers have analyzed this question from a data-driven perspective. We complement these findings investigating if epidemic models calibrate with a systematic procedure are capable of reproducing key spreading cascade properties. We first identify a large-scale, rich dataset from which we can reconstruct the diffusion trail and the underlying network. Secondly, we examine the simple SIR model as a baseline model and conclude that it was unable to generate structurally realistic spreading cascades. We found the same result examining model extensions to which take into account heterogeneities observed in the data. In contrast, other models which take into account time patterns available in the data generate qualitatively more similar cascades. Although one key property was not reproduced in any model, this result highlights the importance of taking time patterns into account. We have also analyzed the impact of the underlying network structure on the models examined. In our data the observed cascades were constrained in time, so we could not rely on the theoretical results relating the asymptotic behavior of the epidemic and network topological features. Performing simulations we assessed the impact of these common topological properties in time-bounded epidemic and identified that the distribution of neighbors of seed nodes had the most impact among the investigated properties in our context. We conclude discussing identifying perspectives opened by this work.

Internet content and Internet users are becoming more linguistically diverse as more people speaking different languages come online and produce content on user-generated content platforms. Several platforms have emerged as truly global platforms with users speaking many different languages and coming from around the world. It is now possible to study human behavior on these platforms using the digital trace data the platforms make available about the content people are authoring. Network literature suggests that people cluster together by language, but also that there is a small average path length between any two people on most Internet platforms (including two speakers of different languages). If so, multilingual users may play critical roles as bridges or brokers on these platforms by connecting clusters of monolingual users together across languages. The large differences in the content available in different languages online underscores the importance of such roles. This thesis studies the roles of multilingual users and platform design on two large, user-generated content platforms: Wikipedia and Twitter. It finds that language has a strong role structuring each platform, that multilingual users do act as linguistic bridges subject to certain limitations, that the size of a language correlates with the roles its speakers play in cross-language connections, and that there is a correlation between activity and multilingualism. In contrast to the general understanding in linguistics of high levels of multilingualism offline, this thesis finds relatively low levels of multilingualism on Twitter (11%) and Wikipedia (15%). The findings have implications for both platform design and social network theory. The findings suggest design strategies to increase multilingualism online through the identification and promotion of multilingual starter tasks, the discovery of related other-language information, and the promotion of user choice in linguistic filtering. While weak-ties have received much attention in the social networks literature, cross-language ties are often not distinguished from same-language weak ties. This thesis finds that cross-language ties are similar to same-language weak ties in that both connect distant parts of the network, have limited bandwidth, and yet transfer a non-trivial amount of information when considered in aggregate. At the same time, cross-language ties are distinct from same-language weak ties for the purposes of information diffusion. In general cross-language ties are smaller in number than same-language ties, but each cross-language tie may convey more diverse information given the large differences in the content available in different languages and the relative ease with which a multilingual speaker may access content in multiple languages compared to a monolingual speaker.

This study proposes a social and emotional disclosure model for understanding the mechanism that explains sharing intimate information on social media (Twitter). Previous research has indicated that some aspects of social, emotional, and attitudinal information processing are involved in disclosure of intimate information. However, these factors have been considered in isolation. This study proposes and tests a theoretically grounded model that brings all of these factors together by combining individual and group social media behaviors and online information processing in the realm of online social movements. The core explanatory model considers the impact of peer response, emotional evaluation, personal relevance, issue orientation, and motivation to post online on intimate information disclosure online. A path analysis building on four Poisson multiple regressions conducted on 28,629 #MeToo tweets evaluates the relationships proposed in the explanatory model. Results indicate that emotional evaluation and motivation to post online have direct, positive impacts on online disclosure. Other factors such as peer response, issue orientation, and personal relevance have negative direct relationships with online disclosure. Motivation to post online mediates the effects of emotional evaluation, issue orientation, and personal relevance on online disclosure while issue orientation mediates the effect of personal relevance on motivation to post online. This study offers findings that have use for practitioners interested in hashtag virality and to social media users interested in social influence and online information sharing.

Les travaux de cette thèse présentent le développement d'algorithmes de classification de documents d'une part, ou d'analyse de réseaux complexes d'autre part, en s'appuyant sur la prétopologie, une théorie qui modélise le concept de proximité. Le premier travail développe un cadre pour la classification de documents en combinant une approche de topicmodeling et la prétopologie. Notre contribution propose d'utiliser des distributions de sujets extraites à partir d'un traitement topic-modeling comme entrées pour des méthodes de classification. Dans cette approche, nous avons étudié deux aspects : déterminer une distance adaptée entre documents en étudiant la pertinence des mesures probabilistes et des mesures vectorielles, et effet réaliser des regroupements selon plusieurs critères en utilisant une pseudo-distance définie à partir de la prétopologie. Le deuxième travail introduit un cadre général de modélisation des Réseaux Complexes en développant une reformulation de la prétopologie stochastique, il propose également un modèle prétopologique de cascade d'informations comme modèle général de diffusion. De plus, nous avons proposé un modèle agent, Textual-ABM, pour analyser des réseaux complexes dynamiques associés à des informations textuelles en utilisant un modèle auteur-sujet et nous avons introduit le Textual-Homo-IC, un modèle de cascade indépendant de la ressemblance, dans lequel l'homophilie est fondée sur du contenu textuel obtenu par un topic-model. / The work of this thesis presents the development of algorithms for document classification on the one hand, or complex network analysis on the other hand, based on pretopology, a theory that models the concept of proximity. The first work develops a framework for document clustering by combining Topic Modeling and Pretopology. Our contribution proposes using topic distributions extracted from topic modeling treatment as input for classification methods. In this approach, we investigated two aspects: determine an appropriate distance between documents by studying the relevance of Probabilistic-Based and Vector-Based Measurements and effect groupings according to several criteria using a pseudo-distance defined from pretopology. The second work introduces a general framework for modeling Complex Networks by developing a reformulation of stochastic pretopology and proposes Pretopology Cascade Model as a general model for information diffusion. In addition, we proposed an agent-based model, Textual-ABM, to analyze complex dynamic networks associated with textual information using author-topic model and introduced Textual-Homo-IC, an independent cascade model of the resemblance, in which homophily is measured based on textual content obtained by utilizing Topic Modeling.

L’objectif des travaux présentés dans cette thèse est d’étudier la diffusion de l’information dans les marchés financiers. Considérant comme établi que les individus sont hétérogènes et à rationalité limitée, nous avons fondé nos travaux sur une catégorie de modèles computationnels dans le but de simuler les actions et les interactions des agents autonomes. Cette catégorie est communément nommée modélisation agent (ABM).Plus concrètement, cette recherche se concentre sur le rôle de l’hétérogénéité des agents dans la diffusion et l’utilisation de l’information. À cet effet, nous avons développé deux structures de marché, qui diffèrent par leur transparence. Dans les chapitres 1 et 2, nous introduisons un marché centralisé, où une partie du carnet d’ordre est accessible (information publique). Dans le chapitre 3, nous développons un marché de gré à gré dans lequel les agents négocient et échangent avec leurs relations. / The piece of work’s aim is to understand information diffusion in financial markets. Starting from the empirical evidences that agents are heterogeneous and bounded rational, we based our investigations on a class of computational models for simulating the actions and interactions of autonomous agents: the agent - based model (ABM). More precisely, this research focuses on the impacts of agents heterogeneity in diffusion and use of information. For this purpose, we developed two market structures, in which the market transparency varies. In the chapters 1 and 2, we introduce a centralised market, where a part of the order-book is available as a public information. In the chapter 3, we build an Over-The-Counter market, where agents bargains with their trading contacts.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-12T14:37:07Z No. of bitstreams: 1 eduardochinelatecosta.pdf: 1021822 bytes, checksum: b72dff6cf071e8de1cb23f6cb7d27245 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T17:15:42Z (GMT) No. of bitstreams: 1 eduardochinelatecosta.pdf: 1021822 bytes, checksum: b72dff6cf071e8de1cb23f6cb7d27245 (MD5) / Made available in DSpace on 2016-01-25T17:15:42Z (GMT). No. of bitstreams: 1 eduardochinelatecosta.pdf: 1021822 bytes, checksum: b72dff6cf071e8de1cb23f6cb7d27245 (MD5) Previous issue date: 2015-02-23 / FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais / Atualmente, há um grande interesse em investigar a dinâmica em Grafos Variantes no Tempo (GVTs). Esses grafos contemplam a evolução temporal, tanto de nós, quanto de arestas. Nesse cenário, de maneira similar a grafos estáticos, o conceito de centralidade geralmente se refere a métricas que avaliam a importância relativa dos vértices. Entretanto, GVTs possibilitam a avaliação da importância dos instantes de tempo (ou estados) de um grafo ao longo de sua existência. Determinar instantes de tempo importantes nesse contexto pode ter aplicações práticas fortes, sendo particularmente úteis para definir melhores momentos para difusão, gerar modelos e prever o comportamento de GVTs. Neste trabalho, nós definimos Centralidade de Tempo em Grafos Variantes no Tempo. A centralidade de tempo avalia a importância relativa dos instantes de tempo. São apresentadas duas métricas de centralidade de tempo voltadas a processos de difusão de informação e uma métrica baseada na disposição das conexões da rede. As métricas foram avaliadas em um conjunto de dados real. Os resultados mostram que os instantes de tempo melhor classificados, de acordo com as métricas criadas, podem tornar o processo de difusão mais rápido e eficiente. Comparado com uma escolha aleatória, o processo de difusão iniciado nos instantes de tempo mais bem classificados pode ser até 2,5 vezes mais rápido, e também pode atingir praticamente o dobro do número de nós na rede em alguns casos. / Currently, there is a great interest in investigating dynamics in Time-Varying Graphs (TVGs). These graphs contemplate the temporal evolution, both nodes and edges. In this scenario, similar to static graphs, centrality usually refers to metrics that assess the relative importance of vertices. However, in TVGs it is possible to assess the importance of time instants (or states) of a graph throughout its existence. Determining important time instants in this context may have strong practical applications and is particularly useful for defining best times to spread, generate models and predict the behavior of TVGs. In this paper, we define time centrality in Time-Varying Graphs. Time centrality evaluates the relative importance of time instants. We present two time centrality metrics focused on information dissemination processes and another based on layout of network connections.. We evaluate metrics we define relying in a real dataset from an hospital environment. Our results show that the best classified time instants, according to created metrics, can make a faster and more efficient diffusion process. Compared to a random choice, the diffusion process starting at best rated time instants can up to 2.5 times faster, and it also can reach almost double the number of nodes in the network in some cases.