This work describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in future. We define event participant as people directly involved in an event regardless of whether they are the agent, recipient or play another role. We present an annotation effort, guidelines and quality analysis with 1,096 event mentions. We discuss the label distributions and event behavior in the annotated corpus. We also explain several features used and a standard supervised machine learning approach to automatically determine if and when the author is a participant of the event in the tweet. We discuss trends in the results obtained and devise important conclusions.

Durant les situations de crise, telles que les catastrophes, le besoin de recherche d’informations (RI) pertinentes partagées dans les microblogs en temps réel est inévitable. Cependant, le grand volume et la variété des flux d’informations partagées en temps réel dans de telles situations compliquent cette tâche. Contrairement aux approches existantes de RI basées sur l’analyse du contenu, nous proposons de nous attaquer à ce problème en nous basant sur les approches centrées utilisateurs tout en levant un certain nombre de verrous méthodologiques et technologiques inhérents : 1) à la collection des données partagées par les utilisateurs à évaluer, 2) à la modélisation de leurs comportements, 3) à l’analyse des comportements, et 4) à la prédiction et le suivi des utilisateurs primordiaux en temps réel. Dans ce contexte, nous détaillons les approches proposées dans cette thèse afin de prédire les utilisateurs primordiaux qui sont susceptibles de partager les informations pertinentes et exclusives ciblées et de permettre aux intervenants d’urgence d’accéder aux informations requises quel que soit le format (i.e. texte, image, vidéo, lien hypertexte) et en temps réel. Ces approches sont centrées sur trois principaux aspects. Nous avons tout d’abord étudié l’efficacité de différentes catégories de mesures issues de la littérature et proposées dans cette thèse pour représenter le comportement des utilisateurs. En nous basant sur les mesures pertinentes résultant de cette étude, nous concevons des nouvelles caractéristiques permettant de mettre en évidence la qualité des informations partagées par les utilisateurs selon leurs comportements. Le deuxième aspect consiste à proposer une approche de modélisation du comportement de chaque utilisateur en nous basant sur les critères suivants : 1) la modélisation des utilisateurs selon l’évolution de l’évènement, 2) la modélisation de l’évolution des activités des utilisateurs au fil du temps à travers une représentation sensible au temps, 3) la sélection des caractéristiques les plus discriminantes pour chaque phase de l’évènement. En se basant sur cette approche de modélisation, nous entraînons différents modèles de prédiction qui apprennent à différencier les comportements des utilisateurs primordiaux de ceux qui ne le sont pas durant les situations de crise. Les algorithmes SVM et MOG-HMMs ont été utilisés durant la phase d’apprentissage. La pertinence et l’efficacité des modèles de prédiction appris ont été validées à l’aide des données collectées par notre système multi-agents MASIR durant deux inondations qui ont eu lieu en France et des vérités terrain appropriées à ces collections. / During crisis events such as disasters, the need of real-time information retrieval (IR) from microblogs remains inevitable. However, the huge amount and the variety of the shared information in real time during such events over-complicate this task. Unlike existing IR approaches based on content analysis, we propose to tackle this problem by using user-centricIR approaches with solving the wide spectrum of methodological and technological barriers inherent to : 1) the collection of the evaluated users data, 2) the modeling of user behavior, 3) the analysis of user behavior, and 4) the prediction and tracking of prominent users in real time. In this context, we detail the different proposed approaches in this dissertation leading to the prediction of prominent users who are susceptible to share the targeted relevant and exclusive information on one hand and enabling emergency responders to have a real-time access to the required information in all formats (i.e. text, image, video, links) on the other hand. These approaches focus on three key aspects of prominent users identification. Firstly, we have studied the efficiency of state-of-the-art and new proposed raw features for characterizing user behavior during crisis events. Based on the selected features, we have designed several engineered features qualifying user activities by considering both their on-topic and off-topic shared information. Secondly, we have proposed a phase-aware user modeling approach taking into account the user behavior change according to the event evolution over time. This user modeling approach comprises the following new novel aspects (1) Modeling microblog users behavior evolution by considering the different event phases (2) Characterizing users activity over time through a temporal sequence representation (3) Time-series-based selection of the most discriminative features characterizing users at each event phase. Thirdly, based on this proposed user modeling approach, we train various prediction models to learn to differentiate between prominent and non-prominent users behavior during crisis event. The learning task has been performed using SVM and MoG-HMMs supervised machine learning algorithms. The efficiency and efficacy of these prediction models have been validated thanks to the data collections extracted by our multi-agents system MASIR during two flooding events who have occured in France and the different ground-truths related to these collections.

The purpose of this study is to analyze the events that occurred throughout the Egyptian Revolution from January 2010 to February 2010 through pragmatic humanism. Tweets will be looked at from the book Tweets from Tahrir to show how the process unfolded. Building on the previous research, the tweets will be looked at through the lens of pragmatic humanism. The study will show how individuals can better the world they live in by experimenting with different methods and adapting to any failures they may encounter. The study will also show how the reach of the individual has become faster and further than previously possible. The elements of pragmatic humanism will be broken down into five main tenets. The study will take a thematic approach in analyzing the tweets through the perspective of the particular tenet. The study will also show the power of individual desires when they are able to combine with the social context of the time. The advent of Twitter has allowed individuals to test and experiment with hypotheses much quicker than before and allows them to make monumental changes to their reality in a much shorter period of time. / Graduation date: 2013

The aim of this research is to shed more light on an aspect identified as a gap in knowledge in the literature; the use of a social networking service as knowledge sharing platform. More specifically, this research sets out to establish if the social networking service Twitter could be used as knowledge-sharing platform in small South African businesses in the Western Cape. A mixed method research design is used. This includes gathering data through questionnaires as well as conducting semi-structured interviews for case study participants. The sample comprises 122 questionnaire participants together with 14 semi-structured interview participants across three small businesses located in the Western Cape Province. Although it is apparent from the study conducted that small businesses are not yet willing to forego traditional platforms such as Email to use Twitter exclusively as a knowledge sharing tool, the researcher proposes a case for using Twitter, which he believes, could take the most advantage of the functions Twitter brings to a small business operation. / School of Computing / M. Sc. (Computing)