Currently social networks focus on the sharing and exchange of opinions, videos, photos, music,news and others informations, one of its objectives is to establish direct and indirect linkswith users. Social networks also promote products, people (their political or artistic image) orinfluential brands.Social networks are changing rapidly, so we're looking to see the evolution of these sharingtools, and see how social networks change over time.We have the opportunity to study the events that occur in social networks thanks to the amount ofdata they produce. In the current market there are tools that allow the analysis of social networks,but most tools are not free, and 100% free tools disappear over time. For this reason we decidedto produce computer tools able to extract and analyse the data of the social networks studied.This study begins with the state of the art, where we describe the context of the problem, thework that led to this study and a summary of the contributions made during the thesis that wepresent briefly in the rest of the abstract.i. First we focus on the geo-linguistic fingerprint and language evolution in Twitter. Accessto content of messages sent by a group of subscribers of a social network may be usedto identify and quantify some features of a group. The feature can represent the level ofinterest in an event or product, or the popularity of an idea, or of a musical hit, or of apolitical figure. The feature can also represent how language is used and transformed,how words are written and how new grammatical rules appear.ii. Then we study the evolution of the cultural phenomenon called meme in social networks.Memes were defined by R. Dowkins as a cultural phenomenon that spreads through nongeneticforms. We examine three of the most popular memes of the internet and examinetheir impact on society in the Mediterranean countries. We use for analysing Google Trends, Topsy (a tool to measure the popularity of words on Twitter) and YouTube toquantify the impact of memes in the Mediterranean ...
Currently social networks focus on the sharing and exchange of opinions, videos, photos, music,news and others informations, one of its objectives is to establish direct and indirect linkswith users. Social networks also promote products, people (their political or artistic image) orinfluential brands.Social networks are changing rapidly, so we're looking to see the evolution of these sharingtools, and see how social networks change over time.We have the opportunity to study the events that occur in social networks thanks to the amount ofdata they produce. In the current market there are tools that allow the analysis of social networks,but most tools are not free, and 100% free tools disappear over time. For this reason we decidedto produce computer tools able to extract and analyse the data of the social networks studied.This study begins with the state of the art, where we describe the context of the problem, thework that led to this study and a summary of the contributions made during the thesis that wepresent briefly in the rest of the abstract.i. First we focus on the geo-linguistic fingerprint and language evolution in Twitter. Accessto content of messages sent by a group of subscribers of a social network may be usedto identify and quantify some features of a group. The feature can represent the level ofinterest in an event or product, or the popularity of an idea, or of a musical hit, or of apolitical figure. The feature can also represent how language is used and transformed,how words are written and how new grammatical rules appear.ii. Then we study the evolution of the cultural phenomenon called meme in social networks.Memes were defined by R. Dowkins as a cultural phenomenon that spreads through nongeneticforms. We examine three of the most popular memes of the internet and examinetheir impact on society in the Mediterranean countries. We use for analysing Google Trends, Topsy (a tool to measure the popularity of words on Twitter) and YouTube toquantify the impact of memes in the Mediterranean society.iii. After that we study the YouTube recommendation graph based on measurements andstochastic tools. We confirm that recommendation lists influence the views of a video.We focus on the recommendation system that boosts the popularity of videos. We buildfirst a graph that captures the recommendation system in YouTube and we study the relationshipbetween the number of views of a video and the average number of views of avideo in its recommendation list.iv. To conclude we describe the online tools available and the tools that we developed duringthe thesis. The online tools Topsy, Trendistic and Google Trends allowed us to analyseplatforms like YouTube and Twitter. We also produced tools based on API's: in Twitterwe used the Streaming function to download and analyse tweets, with the Topsy APIwe studied the evolution of the language and the use of words, and the YouTube's APIsallowed us to describe the behaviour on the lists of recommendations and the popularityof videos. ; Actuellement les réseaux sociaux, se focalisent sur le partage et échange des opinions, vidéos,photos, musique, actualités et autres informations, un de ses objectifs c'est d'établir des liens directs et indirects avec les utilisateurs. Les réseaux sociaux permettent aussi de promouvoir des produits, des personnes (leur image politique ou artistique) ou des marques influentes.Les réseaux sociaux changent rapidement, pour cette raison on cherche a voir l'évolution de ces outils de partage, et aussi voir comment les réseaux sociaux changent avec le temps.On a l'opportunité d'étudier les événements qui se produisent dans les réseaux sociaux grâce à la quantité des données qui se produisent. Dans le marché actuel il y a des outils qui permettent l'analyse des réseaux sociaux, mais la plus part est payante, et les outils 100% gratuits disparaissent avec le temps. Pour cette raison nous avons décidé de produire des outils informatiques capables d'extraire et analyser les données des réseaux sociaux étudiés.Cet étude commence avec l'état de l'art, ou on décrit le contexte du problème abordé, les travaux qui sont à l'origine de cette étude et un résumé des contributions faites au cours de la thèse que nous présentons brièvement dans le reste du résumé.i. D'abord nous nous focalisons dans l'empreinte géo-linguistique et l'évolution du langage en Twitter. L'accès au contenu des messages envoyés par un groupe des abonnées d'un réseau social peut être utilisé pour identifier et quantifier certaines spécificités d'un groupe.La spécificité peut représenter le niveau d'intérêt pour un événement ou un produit, ou la popularité d'une idée, un hit musicale ou une figure politique. La spécificité peut aussi représenter la façon comment le langage est utilisé et transformé, la façon comment les mots sont écrits et la manière comme apparaissent des nouvelles règles de grammaire.ii. Ensuite nous étudions l'évolution du phénomène culturel appelé mème dans les réseaux sociaux. Les mèmes ont été définies par R. Dowkins comme un phénomène culturel qui se propage a travers de formes non génétiques. Nous examinons trois des plus populaires mèmes de l'internet et nous examinons leur impact dans la société dans les Pays Méditerranéens. Nous utilisons pour les analyses Google Trends, Topsy (un outil pour mesurer la popularité des mots sur Twitter) et YouTube pour quantifier l'impact des mèmes dans la société du Méditerranée.iii. Après cela nous étudions le graphe de recommandations de YouTube basées sur les mesures et les outils stochastiques. Nous confirmons que les listes de recommandations influencent les vues d'une vidéo. Nous nous focalisons sur le système de recommandations qui boostent la popularité des vidéos. Nous construisons en premier un graphe qui capture le système de recommandations dans YouTube et nous étudions la relation entre le nombre de vues d'une vidéo et la moyenne du nombre de vues d'une vidéo dans sa liste de recommandation.iv. Pour conclure nous décrivons les outils disponibles en ligne et les outils que nous avons développés pendant l'écriture de la thèse. Les outils en ligne Topsy, Trendistic et GoogleTrends nous ont permit d'analyser des plateformes comme YouTube et Twitter. On a produit aussi des outils basés sur les API's : dans Twitter nous avons utilisé la fonction Streaming pour télécharger et analyser les tweets , avec l'API de Topsy nous avons étudié l'évolution de la langue et l'utilisation des mots , et les API's de YouTube nous ont permis de décrire la façon dont se comportent les listes de recommandations et la popularité des vidéos.
Having access to content of messages sent by some given group of subscribers of a social network may be used to identify (and quantify) some features of that group. The feature can stand for the level of interest in some event or product, or for the popularity of some idea, or a musical hit or of a political figure. The feature can also stand for the way the written language is used and transformed, the way words are spelled and grammer is used. In this paper we shall be interested in identifying features of groups of subscribers that have their geographic location and their language in common. We develop a methodology that allows one to perform such a study using a statistical tool which is freely available, and which makes use of a part of all tweets which twitter makes available for free over the Internet. The methodology is based on the fact that one can differentiate among some geographic areas according to the activity pattern of tweets during the time of the day. We present an application of this methodology to the study of new spellings or of new words created in twitter messages
Having access to content of messages sent by some given group of subscribers of a social network may be used to identify (and quantify) some features of that group. The feature can stand for the level of interest in some event or product, or for the popularity of some idea, or a musical hit or of a political figure. The feature can also stand for the way the written language is used and transformed, the way words are spelled and grammer is used. In this paper we shall be interested in identifying features of groups of subscribers that have their geographic location and their language in common. We develop a methodology that allows one to perform such a study using a statistical tool which is freely available, and which makes use of a part of all tweets which twitter makes available for free over the Internet. The methodology is based on the fact that one can differentiate among some geographic areas according to the activity pattern of tweets during the time of the day. We present an application of this methodology to the study of new spellings or of new words created in twitter messages
