Characterization of cross-posting activity for professional users across Facebook, Twitter and Google+

Professional players in social media (e.g., big companies, politician, athletes, celebrities, etc) are intensively using Online Social Networks (OSNs) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation improvement, etc). Hence, due to the large catalog of existing OSNs, professional players usually count with OSN accounts in different systems. In this context, an interesting question is whether professional users publish the same information across their OSN accounts, or actually they use different OSNs in a different manner. We define as cross-posting activity the action of publishing the same information in two or more OSNs. This paper aims at characterizing the cross-posting activity of professional users across three major OSNs, Facebook, Twitter and Google+. To this end, we perform a large-scale measurement-based analysis across more than 2M posts collected from 616 professional users with active accounts in the three referred OSNs. Then we characterize the phenomenon of cross-posting and analyse the behavioural patterns based on the identified characteristics.This work is partially supported by the European Celtic-Plus project CONVINcE and ITEA3 CAP. as well as the Ministerio de Economia y Competitividad of SPAIN through the project BigDatAAM (FIS2013-47532-C3-3-P) and Horizon 2020 Programme (H2020-DS-2014-1) under Grant Agreement number 653449. We would like thank Reza Motamedi, Reza Rejaie, Roberto Gonzlez and Ruben Cuevas for providing Twitter and Google+ dataset to be used in this study

Popularity Evolution of Professional Users on Facebook

Popularity in social media is an important objective for professional users (e.g. companies, celebrities, and public figures, etc). A simple yet prominent metric utilized to measure the popularity of a user is the number of fans or followers she succeed to attract to her page. Popularity is influenced by several factors which identifying them is an interesting research topic. This paper aims to understand this phenomenon in social media by exploring the popularity evolution for professional users in Facebook. To this end, we implemented a crawler and monitor the popularity evolution trend of 8k most popular professional users on Facebook over a period of 14 months. The collected dataset includes around 20 million popularity values and 43 million posts. We characterized different popularity evolution patterns by clustering the users temporal number of fans and study them from various perspectives including their categories and level of activities. Our observations show that being active and famous correlate positively with the popularity trend

Measurements and analysis of online social networks

Mención InternacionalOnline Social Networks (OSNs) have become the most used Internet applications attracting hundreds of millions active users every day. The large amount of valuable information in OSNs (not even before available) has attracted the research community to design sophisticated techniques to collect, process, interpret and apply these data into a large range of disciplines including Sociology, Marketing, Computer Science, etc. This thesis presents a series of contributions into this incipient area. First, we present a comprehensive framework to perform large scale measurements in OSNs. To this end, the tools and strategies followed to capture representative datasets are described. Furthermore, we present the lessons learned during the crawling process in order to help the reader in a future measurement campaign. Second, using the previous datasets, this thesis address two fundamental aspects that are critical in order to have a clear understanding of the Social Media ecosystem. One the one hand, we characterize the birth and grow of OSNs. In particular, we perform a deep study for a second generation OSN such as Google+ (a OSN released by Google in 2011) and compare its growth with other first generation OSNs such as Twitter. On the other hand, we characterize the information propagation in OSNs in several manners. First, we use Twitter to perform a geographical analysis of the information propagation. Furthermore, we carefully analyze the propagation information in Google+. In particular, we analyze the information propagation trees and the information propagation forests that analyze the propagation information of a piece of content through multiple trees. To the best of our knowledge any previous study has addressed this issue. Finally, the last contribution of this thesis focuses on the analysis of the load received by an OSN system such as Twitter. The conducted research lead to the following main four findings: (i) Second Generation OSNs are expected to grow much faster that the correspondent First Generation OSNs, however they struggle to get users actively engage in the system. This is the case of G+ that is growing at a impressive rate of 350K new users registered per day. However a large fraction (83%) of its users have never been active, and those that present activity are typically significantly less engaged in the system than users in Facebook or Twitter. (ii) The information propagates faster but following shorter paths in Twitter than in G+. This is a consequence of the way in which information is shown in each system. Secuentialbased systems such as Twitter force short-term conversations among their users whereas Selective-based systems such as those used in G+ or Facebook chooses which content to show to each user based on his preferences, volume of interactions with other users, etc. This helps to prolong the lifespan of conversations in the OSN.(iii) Our analysis of the geographical propagation of information in Twitter reveals that users tend to send tweets from a sole geographical location. Furthermore, the level of locality associated to the social relationships varies across countries and thus for some countries like Brazil it is more likely that the information remains local than for other countries such as Australia. (iv) Our analysis of the load of Twitter system indicates that the arrival process of tweets follows a model similar to a Gaussian with a noticeable day-night pattern. In short the work presented in this thesis allows advancing our knowledge of the Social Media ecosystem in essential directions such as the formation and growth of OSNs or the propagation of information in these systems. The important reported findings will help to develop new services on top of OSNs.Las redes sociales (OSNs por sus siglas en inglés) se han convertido en una de las aplicaciones más usadas de Internet atrayendo cientos de millones de usuarios cada día. La gran cantidad de información valiosa en las redes sociales (que antes no estaba disponible) ha llevado a la comunidad cientifica a diseñar sofisticadas tecnicas para recoger, procesar, interpretar y usar esos datos en diferentes disciplinas incluyendo sociología, marketing, informática, etc. Esta tesis presenta una serie de contribuciones en esta incipiente área. Primero, presentamos un completo marco que permite realizar medidas a gran escala de redes sociales. Con este propósito, el documento describe las herramientas y estrategias seguidas para obtener un conjunto de datos representativo. Tambien, añadimos las lecciones aprendidas durante el proceso de obtención de datos. Estas lecciones pueden ayudar al lector en una futura campaña de medidas sobre redes sociales. Segundo, usando el conjunto de datos obtenido con las herramientas descritas, esta tesis aborda dos aspectos fundamentales que son críticos para entender el ecosistema de las redes sociales. Por un lado, caracterizamos el nacimiento y crecimiento de redes sociales. En particular, llevamos a cabo un análisis en profundidad de una red social de segunda generación como Google+ (una red social lanzada por Google en 2011) y comparamos su crecimiento con otras redes sociales de primera generación como Twitter. Por otro lado caracterizamos la propagación de la información en redes sociales de diferentes maneras. Primero, usamos Twitter para llevar a cabo un analisis geográfico de la propagación de la información. También analizamos la propagación de la información en Google+. En particular, analizamos los árboles de propagación de información y los bosques de propagación de información que incluyen la información sobre la propagación de una misma pieza de contenido a traves de diferentes árboles. A nuestro saber, este es el primer estudio que aborda esta cuestión. Por último, analizamos la carga soportada por una red social como Twitter. La investigación realizada nos lleva a los siguientes 4 resultados principales: (i) Es de esperar que las redes sociales de segunda generación crezcan mucho más rápido que las correspondientes de primera generaci´on, sin embargo, estas tiene muchas dificultades para mantener los usuarios involucrados en el sistema. Este es el caso de G+ que está creciendo al impresionante ritmo de 350K nuevos usuarios registrados por dia. Sin embargo una gran fracción (83%) de ellos no ha llegado nunca a ser activos y los que presentan actividad presentan en general una actividad menos que los usuarios de Facebook o Twitter. (ii) La información se propaga más rápido pero siguiendo caminos más cortos en Twitter que en G+. Esto es una consecuencia de la manera en la que la información es mostrada en cada sistema: sistema secuenciales como en Twitter fuerzan que la información sea consumida al instante mientras que sistemas selectivos como el usado en G+ o Facebook, donde la información que se muestra depende las preferencias de los usuarios y el volumen de interacción con otros usuarios ayuda a prolongar la vida del contenido en la red social. (iii) Nuestro analisis de la propagacion geográfica de la información en Twitter revela que los usuarios suelen enviar tweets desde una única localización geográfica. Además, el nivel de geolocalización asociada a las relaciones sociales varía entre países y encontramos algunos paises, como Brasil, donde es más que la información se mantenga local que en otros como Australia. (iv) Nuestro análisis de la carga de Twitter indica que el proceso de llegada de tweets sigue un modelo gausiano con un marcado patrón día-noche. En definitiva, el trabajo presentado en este tesis permite aumentar nuestro conocimiento sobre el ecosistema de las redes sociales en direcciones esenciales como pueden ser la formación y crecimiento de redes sociales o la propagación de información en estos sistemas. Los resultados reportados ayudarán a desarrollar nuevos servicios sobre las redes sociales.Programa en Ingeniería TelemáticaPresidente: Antonio Fernández Anta; Vocal: Marco Mellia; Secretario: Francisco Valera Pinto

Two Notions of Privacy Online

Users of social networking websites tend to disclose much personal information online yet seem to retain some form of an expectation of privacy. Is this expectation of privacy always unreasonable? How do users of online social networks define their expectations of privacy online? These questions were the impetus behind an empirical study, the findings of which are presented in this Article. The project, simultaneously conducted in Canada, at Ryerson University, and in the United States, at the University of Miami, consisted of a survey regarding personal information protection and expectations of privacy on online social networks (OSNs). Approximately 2,500 young adults between the ages of 18 and 24 were surveyed about the personal information they post online, the measures they take to protect such information, and their concerns, if any, regarding their personal information. Respondents also reacted to several hypothetical scenarios in which their privacy was breached on an OSN by measures both within and beyond their control. The theoretical assumption underlying this research project is that two prevalent and competing notions of privacy online exist: one rooted in control and the other in dignity. Of the two, the idea of privacy as control over one\u27s personal information has, to date, been predominant. Legislation, regulation, corporate policy, and technology are often analyzed and evaluated in terms of the measure of control offered to individuals over their personal information. Leading OSNs, such as Facebook and MySpace, propagate a notion of privacy as user control. However, online social networking poses a fundamental challenge to the theory of privacy as control. A high degree of control cannot preclude the possibility that online socializers would post unflattering, defamatory, or personal information about each other, and that this information would in turn be available to a large, if not unrestricted, online audience. Many online socializers post personal information seemingly without much concern over the loss of control, yet it seems that online socializers react with indignation when their personal information is accessed, used, or disclosed by individuals perceived to be outside their social network. The findings presented here indicate indeed that online socializers have developed a new and arguably legitimate notion of privacy online, that if accepted by OSNs, will offer online socializers both control and protection of their dignity and reputation. We call this notion network privacy. According to network privacy, information is considered by online socializers to be private as long as it is not disclosed outside of the network to which they initially disclosed it, if it originates with them, or as long as it does not affect their established online personae, if it originates with others. OSNs, as businesses profiting from socializing online, are best positioned to offer online socializers, often the young and vulnerable, effective protection in accordance with their notion of network privacy above and beyond regular measures of personal information control, and they should be required to do so

Robust, Resilient Networked Communication in Challenged Environments

In challenged environments, digital communication infrastructure may be difficult or even impossible to access. This is especially true in rural and developing regions, as well as in any region during a time of political or environmental crisis. We advance the state of the art in wireless networking and security to design networks and applications that rapidly assess changing networking conditions to restore communication and provide local situational awareness. This dissertation examines new systems for responding to current and emerging needs for wireless networks. This work looks across the wireless ecosystem of widely deployed standards. We develop new tools to improve network assessment and to provide robust and reliable network communication. By incorporating new technological breakthroughs, such as the wide commercial success of Unmanned Aircraft Systems (UAS), we introduce novel methods and systems for existing wireless standards for these challenged networks. We assess how existing technologies and standards function in difficult environments: lacking end-end Internet connectivity, experiencing overload or other resource constraints, and operating in three dimensional space. Through this lens, we demonstrate how to optimize networks to serve marginalized communities outside of first world urban cities and make our networks resilient to natural and political crisis that threaten communication

DYNAMICS OF IDENTITY THREATS IN ONLINE SOCIAL NETWORKS: MODELLING INDIVIDUAL AND ORGANIZATIONAL PERSPECTIVES

This dissertation examines the identity threats perceived by individuals and organizations in Online Social Networks (OSNs). The research constitutes two major studies. Using the concepts of Value Focused Thinking and the related methodology of Multiple Objectives Decision Analysis, the first research study develops the qualitative and quantitative value models to explain the social identity threats perceived by individuals in Online Social Networks. The qualitative value model defines value hierarchy i.e. the fundamental objectives to prevent social identity threats and taxonomy of user responses, referred to as Social Identity Protection Responses (SIPR), to avert the social identity threats. The quantitative value model describes the utility of the current social networking sites and SIPR to achieve the fundamental objectives for averting social identity threats in OSNs. The second research study examines the threats to the external identity of organizations i.e. Information Security Reputation (ISR) in the aftermath of a data breach. The threat analysis is undertaken by examining the discourses related to the data breach at Home Depot and JPMorgan Chase in the popular microblogging website, Twitter, to identify: 1) the dimensions of information security discussed in the Twitter postings; 2) the attribution of data breach responsibility and the related sentiments expressed in the Twitter postings; and 3) the subsequent diffusion of the tweets that threaten organizational reputation

Digital fingerprinting for identifying malicious collusive groups on Twitter

Propagation of malicious code on online social networks (OSN) is often a coordinated effort by collusive groups of malicious actors hiding behind multiple online identities (or digital personas). Increased interaction in OSN have made them reliable for the efficient orchestration of cyber-attacks such as phishing click bait and drive-by downloads. URL shortening enables obfuscation of such links to malicious websites and massive interaction with such embedded malicious links in OSN guarantees maximum reach. These malicious links lure users to malicious endpoints where attackers can exploit system vulnerabilities. Identifying the organised groups colluding to spread malware is non-trivial owing to the fluidity and anonymity of criminal digital personas on OSN. This paper proposes a methodology for identifying such organised groups of criminal actors working together to spread malicious links on OSN. Our approach focuses on understanding malicious users as ‘digital criminal personas’ and characteristics of their online existence. We first identify those users engaged in propagating malicious links on OSN platforms, and further develop a methodology to create a digital fingerprint for each malicious OSN account/digital persona. We create similarity clusters of malicious actors based on these unique digital fingerprints to establish ‘collusive’ behaviour. We evaluate the ability of a cluster-based approach on OSN digital fingerprinting to identify collusive behaviour in OSN by estimating within-cluster similarity measures and testing it on a ground truth dataset of five known colluding groups on Twitter. Our results show that our digital fingerprints can identify 90% of cyber-personas engaged in collusive behaviour 75% of collusion in a given sample set