Studying Social Networks at Scale: Macroscopic Anatomy of the Twitter Social Graph

Twitter is one of the largest social networks using exclusively directed links among accounts. This makes the Twitter social graph much closer to the social graph supporting real life communications than, for instance, Facebook. Therefore, understanding the structure of the Twitter social graph is interesting not only for computer scientists, but also for researchers in other fields, such as sociologists. However, little is known about how the information propagation in Twitter is constrained by its inner structure. In this paper, we present an in-depth study of the macroscopic structure of the Twitter social graph unveiling the highways on which tweets propagate, the specific user activity associated with each component of this macroscopic structure, and the evolution of this macroscopic structure with time for the past 6 years. For this study, we crawled Twitter to retrieve all accounts and all social relationships (follow links) among accounts; the crawl completed in July 2012 with 505 million accounts interconnected by 23 billion links. Then, we present a methodology to unveil the macroscopic structure of the Twitter social graph. This macroscopic structure consists of 8 components defined by their connectivity characteristics. Each component group users with a specific usage of Twitter. For instance, we identified components gathering together spammers, or celebrities. Finally, we present a method to approximate the macroscopic structure of the Twitter social graph in the past, validate this method using old datasets, and discuss the evolution of the macroscopic structure of the Twitter social graph during the past 6 years.Comment: ACM Sigmetrics 2014 (2014

How to Network in Online Social Networks

In this paper, we consider how to maximize users' influence in Online Social Networks (OSNs) by exploiting social relationships only. Our first contribution is to extend to OSNs the model of Kempe et al. [1] on the propagation of information in a social network and to show that a greedy algorithm is a good approximation of the optimal algorithm that is NP-hard. However, the greedy algorithm requires global knowledge, which is hardly practical. Our second contribution is to show on simulations on the full Twitter social graph that simple and practical strategies perform close to the greedy algorithm.Comment: NetSciCom 2014 - The Sixth IEEE International Workshop on Network Science for Communication Networks (2014

Comment se propagent les informations sur Twitter ?

This thesis presents the measurement study of Online Social Networks focusing on Twitter. Twitter is one of the largest social networks using exclusively directed links among accounts. This makes the Twitter social graph much closer to the social graph supporting real life communications than, for instance, Facebook. Therefore, understanding the structure of the Twitter social graph and the way information propagates through it is interesting not only for computer scientists, but also for researchers in other fields, such as sociologists. However, littles is known about the information propagation in Twitter. In the first part, we present an in-depth study of the macroscopic structure of the Twitter social graph. In the second part, we study the propagation of the news media articles shared on Twitter. In the third part we present an experimental study of graph sampling.Cette thèse présente une étude sur la mesure des réseaux sociaux en ligne avec un accent particulier sur Twitter qui est l'un des plus grands réseaux sociaux. Twitter utilise exclusivement des liens dirigés entre les comptes. Cela rend le graphe social de Twitter beaucoup plus proche que Facebok du graphe social représentant les communications dans la vie réelle. Par conséquent, la compréhension de la structure du graphe social de Twitter et de la manière dont les informations se propagent dans le graphe est intéressant non seulement pour les informaticiens, mais aussi pour les chercheurs dans d'autres domaines, tels que la sociologie. Cependant, on sait peu de choses sur la propagation de l'information sur Twitter

The Complete Picture of the Twitter Social Graph

International audienceIn this work, we collected the entire Twitter social graph that consists of 537 million Twitter accounts connected by 23.95 billion links, and performed a preliminary analysis of the collected data. In order to collect the social graph, we implemented a distributed crawler on the PlanetLab infrastructure that collected all information in 4 months. Our preliminary analysis already revealed some interesting properties. Whereas there are 537 million Twitter accounts, only 268 million already sent at least one tweet and no more than 54 million have been recently active. In addition, 40% of the accounts are not followed by anybody and 25% do not follow anybody. Finally, we found that the Twitter policies, but also social conventions (like the follow-back convention) have a huge impact on the structure of the Twitter social graph

Social Clicks: What and Who Gets Read on Twitter?

International audienceOnline news domains increasingly rely on social media to drive traffic to their websites. Yet we know surprisingly little about how a social media conversation mentioning an online article actually generates clicks. Sharing behaviors, in contrast, have been fully or partially available and scrutinized over the years. While this has led to multiple assumptions on the diffusion of information, each assumption was designed or validated while ignoring actual clicks. We present a large scale, unbiased study of social clicks - that is also the first data of its kind - gathering a month of web visits to online resources that are located in 5 leading news domains and that are mentioned in the third largest social media by web referral (Twitter). Our dataset amounts to 2.8 million shares, together responsible for 75 billion potential views on this social media, and 9.6 million actual clicks to 59,088 unique resources. We design a reproducible methodology and carefully correct its biases. As we prove, properties of clicks impact multiple aspects of information diffusion, all previously unknown. (i) Secondary resources, that are not promoted through headlines and are responsible for the long tail of content popularity, generate more clicks both in absolute and relative terms. (ii) Social media attention is actually long-lived, in contrast with temporal evolution estimated from shares or receptions. (iii) The actual influence of an intermediary or a resource is poorly predicted by their share count, but we show how that prediction can be made more precise

How information propagates on Twitter ?

Cette thèse présente une étude sur la mesure des réseaux sociaux en ligne avec un accent particulier sur Twitter qui est l'un des plus grands réseaux sociaux. Twitter utilise exclusivement des liens dirigés entre les comptes. Cela rend le graphe social de Twitter beaucoup plus proche que Facebok du graphe social représentant les communications dans la vie réelle. Par conséquent, la compréhension de la structure du graphe social de Twitter et de la manière dont les informations se propagent dans le graphe est intéressant non seulement pour les informaticiens, mais aussi pour les chercheurs dans d'autres domaines, tels que la sociologie. Cependant, on sait peu de choses sur la propagation de l'information sur Twitter.This thesis presents the measurement study of Online Social Networks focusing on Twitter. Twitter is one of the largest social networks using exclusively directed links among accounts. This makes the Twitter social graph much closer to the social graph supporting real life communications than, for instance, Facebook. Therefore, understanding the structure of the Twitter social graph and the way information propagates through it is interesting not only for computer scientists, but also for researchers in other fields, such as sociologists. However, littles is known about the information propagation in Twitter. In the first part, we present an in-depth study of the macroscopic structure of the Twitter social graph. In the second part, we study the propagation of the news media articles shared on Twitter. In the third part we present an experimental study of graph sampling

Sampling Online Social Networks: An Experimental Study of Twitter

International audienceOnline social networks (OSNs) are an important source of information for scientists in different fields such as computer science, sociology, economics, etc. However, it is hard to study OSNs as they are very large. For instance, Facebook has 1.28 billion active users in March 2014 and Twitter claims 255 million active users in April 2014. Also, com-panies take measures to prevent crawls of their OSNs and refrain from sharing their data with the research community. For these reasons, we argue that sampling techniques will be the best technique to study OSNs in the future. In this work, we take an experimental approach to study the characteristics of well-known sampling techniques on a full social graph of Twitter crawled in 2012 [2]. Our contri-bution is to evaluate the behavior of these techniques on a real directed graph by considering two sampling scenarios: (a) obtaining most popular users (b) obtaining an unbiased sample of users, and to find the most suitable sampling tech-niques for each scenario