Event summarization on social media stream: retrospective and prospective tweet summarization

Le contenu généré dans les médias sociaux comme Twitter permet aux utilisateurs d'avoir un aperçu rétrospectif d'évènement et de suivre les nouveaux développements dès qu'ils se produisent. Cependant, bien que Twitter soit une source d'information importante, il est caractérisé par le volume et la vélocité des informations publiées qui rendent difficile le suivi de l'évolution des évènements. Pour permettre de mieux tirer profit de ce nouveau vecteur d'information, deux tâches complémentaires de recherche d'information dans les médias sociaux ont été introduites : la génération de résumé rétrospectif qui vise à sélectionner les tweets pertinents et non redondant récapitulant "ce qui s'est passé" et l'envoi des notifications prospectives dès qu'une nouvelle information pertinente est détectée. Notre travail s'inscrit dans ce cadre. L'objectif de cette thèse est de faciliter le suivi d'événement, en fournissant des outils de génération de synthèse adaptés à ce vecteur d'information. Les défis majeurs sous-jacents à notre problématique découlent d'une part du volume, de la vélocité et de la variété des contenus publiés et, d'autre part, de la qualité des tweets qui peut varier d'une manière considérable. La tâche principale dans la notification prospective est l'identification en temps réel des tweets pertinents et non redondants. Le système peut choisir de retourner les nouveaux tweets dès leurs détections où bien de différer leur envoi afin de s'assurer de leur qualité. Dans ce contexte, nos contributions se situent à ces différents niveaux : Premièrement, nous introduisons Word Similarity Extended Boolean Model (WSEBM), un modèle d'estimation de la pertinence qui exploite la similarité entre les termes basée sur le word embedding et qui n'utilise pas les statistiques de flux. L'intuition sous- jacente à notre proposition est que la mesure de similarité à base de word embedding est capable de considérer des mots différents ayant la même sémantique ce qui permet de compenser le non-appariement des termes lors du calcul de la pertinence. Deuxièmement, l'estimation de nouveauté d'un tweet entrant est basée sur la comparaison de ses termes avec les termes des tweets déjà envoyés au lieu d'utiliser la comparaison tweet à tweet. Cette méthode offre un meilleur passage à l'échelle et permet de réduire le temps d'exécution. Troisièmement, pour contourner le problème du seuillage de pertinence, nous utilisons un classificateur binaire qui prédit la pertinence. L'approche proposée est basée sur l'apprentissage supervisé adaptatif dans laquelle les signes sociaux sont combinés avec les autres facteurs de pertinence dépendants de la requête. De plus, le retour des jugements de pertinence est exploité pour re-entrainer le modèle de classification. Enfin, nous montrons que l'approche proposée, qui envoie les notifications en temps réel, permet d'obtenir des performances prometteuses en termes de qualité (pertinence et nouveauté) avec une faible latence alors que les approches de l'état de l'art tendent à favoriser la qualité au détriment de la latence. Cette thèse explore également une nouvelle approche de génération du résumé rétrospectif qui suit un paradigme différent de la majorité des méthodes de l'état de l'art. Nous proposons de modéliser le processus de génération de synthèse sous forme d'un problème d'optimisation linéaire qui prend en compte la diversité temporelle des tweets. Les tweets sont filtrés et regroupés d'une manière incrémentale en deux partitions basées respectivement sur la similarité du contenu et le temps de publication. Nous formulons la génération du résumé comme étant un problème linéaire entier dans lequel les variables inconnues sont binaires, la fonction objective est à maximiser et les contraintes assurent qu'au maximum un tweet par cluster est sélectionné dans la limite de la longueur du résumé fixée préalablement.User-generated content on social media, such as Twitter, provides in many cases, the latest news before traditional media, which allows having a retrospective summary of events and being updated in a timely fashion whenever a new development occurs. However, social media, while being a valuable source of information, can be also overwhelming given the volume and the velocity of published information. To shield users from being overwhelmed by irrelevant and redundant posts, retrospective summarization and prospective notification (real-time summarization) were introduced as two complementary tasks of information seeking on document streams. The former aims to select a list of relevant and non-redundant tweets that capture "what happened". In the latter, systems monitor the live posts stream and push relevant and novel notifications as soon as possible. Our work falls within these frameworks and focuses on developing a tweet summarization approaches for the two aforementioned scenarios. It aims at providing summaries that capture the key aspects of the event of interest to help users to efficiently acquire information and follow the development of long ongoing events from social media. Nevertheless, tweet summarization task faces many challenges that stem from, on one hand, the high volume, the velocity and the variety of the published information and, on the other hand, the quality of tweets, which can vary significantly. In the prospective notification, the core task is the relevancy and the novelty detection in real-time. For timeliness, a system may choose to push new updates in real-time or may choose to trade timeliness for higher notification quality. Our contributions address these levels: First, we introduce Word Similarity Extended Boolean Model (WSEBM), a relevance model that does not rely on stream statistics and takes advantage of word embedding model. We used word similarity instead of the traditional weighting techniques. By doing this, we overcome the shortness and word mismatch issues in tweets. The intuition behind our proposition is that context-aware similarity measure in word2vec is able to consider different words with the same semantic meaning and hence allows offsetting the word mismatch issue when calculating the similarity between a tweet and a topic. Second, we propose to compute the novelty score of the incoming tweet regarding all words of tweets already pushed to the user instead of using the pairwise comparison. The proposed novelty detection method scales better and reduces the execution time, which fits real-time tweet filtering. Third, we propose an adaptive Learning to Filter approach that leverages social signals as well as query-dependent features. To overcome the issue of relevance threshold setting, we use a binary classifier that predicts the relevance of the incoming tweet. In addition, we show the gain that can be achieved by taking advantage of ongoing relevance feedback. Finally, we adopt a real-time push strategy and we show that the proposed approach achieves a promising performance in terms of quality (relevance and novelty) with low cost of latency whereas the state-of-the-art approaches tend to trade latency for higher quality. This thesis also explores a novel approach to generate a retrospective summary that follows a different paradigm than the majority of state-of-the-art methods. We consider the summary generation as an optimization problem that takes into account the topical and the temporal diversity. Tweets are filtered and are incrementally clustered in two cluster types, namely topical clusters based on content similarity and temporal clusters that depends on publication time. Summary generation is formulated as integer linear problem in which unknowns variables are binaries, the objective function is to be maximized and constraints ensure that at most one post per cluster is selected with respect to the defined summary length limit

Survey of context provisioning middleware

In the scope of ubiquitous computing, one of the key issues is the awareness of context, which includes diverse aspects of the user's situation including his activities, physical surroundings, location, emotions and social relations, device and network characteristics and their interaction with each other. This contextual knowledge is typically acquired from physical, virtual or logical sensors. To overcome problems of heterogeneity and hide complexity, a significant number of middleware approaches have been proposed for systematic and coherent access to manifold context parameters. These frameworks deal particularly with context representation, context management and reasoning, i.e. deriving abstract knowledge from raw sensor data. This article surveys not only related work in these three categories but also the required evaluation principles. © 2009-2012 IEEE

Understanding receptivity to interruptions in mobile human-computer interaction

Interruptions have a profound impact on our attentional orientation in everyday life. Recent advances in mobile information technology increase the number of potentially disruptive notifications on mobile devices by an increasing availability of services. Understanding the contextual intricacies that make us receptive to these interruptions is paramount to devising technology that supports interruption management. This thesis makes a number of contributions to the methodology of studying mobile experiences in situ, understanding receptivity to interruptions, and designing context-sensitive systems. This thesis presents a series of real-world studies that investigate opportune moments for interruptions in mobile settings. In order to facilitate the study of the multi-faceted ways opportune moments surface from participants' involvement in the world this thesis develops: - a model of the contextual factors that interact to guide receptivity to interruptions, and - an adaptation of the Experience-Sampling Method (ESM) to capture behavioural response to interruptions in situ. In two naturalistic experiments, participants' experiences of being interrupted on a mobile phone are sampled as they go about their everyday lives. In a field study, participants' experiences are observed and recorded as they use a notification-driven mobile application to create photo-stories in a theme park. Experiment 1 explores the effects of content and time of delivery of the interruption. The results show that receptivity to text messages is significantly affected by message content, while scheduling one's own interruption times in advance does not improve receptivity over randomly timed interruptions. Experiment 2 investigates the hypothesis that opportune moments to deliver notifications are located at the endings of episodes of mobile interaction such as texting and calling. This notification strategy is supported by significant effects in behavioural measures of receptivity, while self-reports and interviews reveal complexities in the subjective experience of the interruption. By employing a mixed methods approach of interviews, observations and an analysis of system logs in the field study, it is shown that participants appreciated location-based notifications as prompts to foreground the application during relative 'downtimes' from other activities. However, an unexpected quantity of redundant notifications meant that visitors soon habituated to and eventually ignored them, which suggests careful, sparing use of notifications in interactive experiences. Overall, the studies showed that contextual mediation of the timing of interruptions (e.g. by phone activity in Experiment 2 and opportune places in the field study) is more likely to lead to interruptions at opportune moments than when participants schedule their own interruptions. However, momentary receptivity and responsiveness to an interruption is determined by the complex and situated interactions of local and relational contextual factors. These contextual factors are captured in a model of receptivity that underlies the interruption process. The studies highlight implications for the design of systems that seek to manage interruptions by adapting the timing of interruptions to the user's situation. In particular, applications to manage interruptions in personal communication and pervasive experiences are considered

Architectures for ubiquitous 3D on heterogeneous computing platforms

Today, a wide scope for 3D graphics applications exists, including domains such as scientific visualization, 3D-enabled web pages, and entertainment. At the same time, the devices and platforms that run and display the applications are more heterogeneous than ever. Display environments range from mobile devices to desktop systems and ultimately to distributed displays that facilitate collaborative interaction. While the capability of the client devices may vary considerably, the visualization experiences running on them should be consistent. The field of application should dictate how and on what devices users access the application, not the technical requirements to realize the 3D output. The goal of this thesis is to examine the diverse challenges involved in providing consistent and scalable visualization experiences to heterogeneous computing platforms and display setups. While we could not address the myriad of possible use cases, we developed a comprehensive set of rendering architectures in the major domains of scientific and medical visualization, web-based 3D applications, and movie virtual production. To provide the required service quality, performance, and scalability for different client devices and displays, our architectures focus on the efficient utilization and combination of the available client, server, and network resources. We present innovative solutions that incorporate methods for hybrid and distributed rendering as well as means to manage data sets and stream rendering results. We establish the browser as a promising platform for accessible and portable visualization services. We collaborated with experts from the medical field and the movie industry to evaluate the usability of our technology in real-world scenarios. The presented architectures achieve a wide coverage of display and rendering setups and at the same time share major components and concepts. Thus, they build a strong foundation for a unified system that supports a variety of use cases.Heutzutage existiert ein großer Anwendungsbereich für 3D-Grafikapplikationen wie wissenschaftliche Visualisierungen, 3D-Inhalte in Webseiten, und Unterhaltungssoftware. Gleichzeitig sind die Geräte und Plattformen, welche die Anwendungen ausführen und anzeigen, heterogener als je zuvor. Anzeigegeräte reichen von mobilen Geräten zu Desktop-Systemen bis hin zu verteilten Bildschirmumgebungen, die eine kollaborative Anwendung begünstigen. Während die Leistungsfähigkeit der Geräte stark schwanken kann, sollten die dort laufenden Visualisierungen konsistent sein. Das Anwendungsfeld sollte bestimmen, wie und auf welchem Gerät Benutzer auf die Anwendung zugreifen, nicht die technischen Voraussetzungen zur Erzeugung der 3D-Grafik. Das Ziel dieser Thesis ist es, die diversen Herausforderungen zu untersuchen, die bei der Bereitstellung von konsistenten und skalierbaren Visualisierungsanwendungen auf heterogenen Plattformen eine Rolle spielen. Während wir nicht die Vielzahl an möglichen Anwendungsfällen abdecken konnten, haben wir eine repräsentative Auswahl an Rendering-Architekturen in den Kernbereichen wissenschaftliche Visualisierung, web-basierte 3D-Anwendungen, und virtuelle Filmproduktion entwickelt. Um die geforderte Qualität, Leistung, und Skalierbarkeit für verschiedene Client-Geräte und -Anzeigen zu gewährleisten, fokussieren sich unsere Architekturen auf die effiziente Nutzung und Kombination der verfügbaren Client-, Server-, und Netzwerkressourcen. Wir präsentieren innovative Lösungen, die hybrides und verteiltes Rendering als auch das Verwalten der Datensätze und Streaming der 3D-Ausgabe umfassen. Wir etablieren den Web-Browser als vielversprechende Plattform für zugängliche und portierbare Visualisierungsdienste. Um die Verwendbarkeit unserer Technologie in realitätsnahen Szenarien zu testen, haben wir mit Experten aus der Medizin und Filmindustrie zusammengearbeitet. Unsere Architekturen erreichen eine umfassende Abdeckung von Anzeige- und Rendering-Szenarien und teilen sich gleichzeitig wesentliche Komponenten und Konzepte. Sie bilden daher eine starke Grundlage für ein einheitliches System, das eine Vielzahl an Anwendungsfällen unterstützt