Temporal Analysis of Activity Patterns of Editors in Collaborative Mapping Project of OpenStreetMap

In the recent years Wikis have become an attractive platform for social studies of the human behaviour. Containing millions records of edits across the globe, collaborative systems such as Wikipedia have allowed researchers to gain a better understanding of editors participation and their activity patterns. However, contributions made to Geo-wikis_wiki-based collaborative mapping projects_ differ from systems such as Wikipedia in a fundamental way due to spatial dimension of the content that limits the contributors to a set of those who posses local knowledge about a specific area and therefore cross-platform studies and comparisons are required to build a comprehensive image of online open collaboration phenomena. In this work, we study the temporal behavioural pattern of OpenStreetMap editors, a successful example of geo-wiki, for two European capital cities. We categorise different type of temporal patterns and report on the historical trend within a period of 7 years of the project age. We also draw a comparison with the previously observed editing activity patterns of Wikipedia.Comment: Submitte

Bursty egocentric network evolution in Skype

In this study we analyze the dynamics of the contact list evolution of millions of users of the Skype communication network. We find that egocentric networks evolve heterogeneously in time as events of edge additions and deletions of individuals are grouped in long bursty clusters, which are separated by long inactive periods. We classify users by their link creation dynamics and show that bursty peaks of contact additions are likely to appear shortly after user account creation. We also study possible relations between bursty contact addition activity and other user-initiated actions like free and paid service adoption events. We show that bursts of contact additions are associated with increases in activity and adoption - an observation that can inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013

Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership

Detecting community structure in social networks is a fundamental problem empowering us to identify groups of actors with similar interests. There have been extensive works focusing on finding communities in static networks, however, in reality, due to dynamic nature of social networks, they are evolving continuously. Ignoring the dynamic aspect of social networks, neither allows us to capture evolutionary behavior of the network nor to predict the future status of individuals. Aside from being dynamic, another significant characteristic of real-world social networks is the presence of leaders, i.e. nodes with high degree centrality having a high attraction to absorb other members and hence to form a local community. In this paper, we devised an efficient method to incrementally detect communities in highly dynamic social networks using the intuitive idea of importance and persistence of community leaders over time. Our proposed method is able to find new communities based on the previous structure of the network without recomputing them from scratch. This unique feature, enables us to efficiently detect and track communities over time rapidly. Experimental results on the synthetic and real-world social networks demonstrate that our method is both effective and efficient in discovering communities in dynamic social networks

Google+ or Google-?: Dissecting the Evolution of the New OSN in its First Year

In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question that "whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter?". This paper tackles the above question by presenting a detailed characterization of G+ based on large scale measurements. We identify the main components of G+ structure, characterize the key features of their users and their evolution over time. We then conduct detailed analysis on the evolution of connectivity and activity among users in the largest connected component (LCC) of G+ structure, and compare their characteristics with other major OSNs. We show that despite the dramatic growth in the size of G+, the relative size of LCC has been decreasing and its connectivity has become less clustered. While the aggregate user activity has gradually increased, only a very small fraction of users exhibit any type of activity. To our knowledge, our study offers the most comprehensive characterization of G+ based on the largest collected data sets.Comment: WWW 201

FROM SMALL-WORLDS TO BIG DATA:TEMPORAL AND MULTIDIMENSIONAL ASPECTS OF HUMAN NETWORKS

In this thesis we address the close interplay among mobility, offline relationships and online interactions and the related human networks at different dimensional scales and temporal granularities. By generally adopting a data-driven approach, we move from small datasets about physical interactions mediated by human-carried devices, describing small social realities, to large-scale graphs that evolve over time, as well as from human mobility trajectories to face-to-face contacts occurring in different geographical contexts. We explore in depth the relation between human mobility and the social structure induced by the overlapping of different people's trajectories on GPS traces collected in urban and metropolitan areas. We define the notions of geo-location and geo-community which are operational in describing in a unique framework both spatial and social aspects of human behavior. Through the concept of geo-community we model the human mobility adopting a bipartite graph. Thanks to this graph representation we can generate a social structure that is plausible w.r.t. the real interactions. In general the modeling approach have the merit for reporting the mobility in a graph-theoretic framework making the study of the interplay mobility/sociality more affordable and intuitive. Our modeling approach also results in a mobility model, Geo-CoMM, which lies on and exploits the idea of geo-community. The model represents a particular instance of a general framework we provide. A framework where the social structure behind the preferred-location based mobility models emerges. We validate Geo-CoMM on spatial, temporal, pairwise connectivity and social features showing that it reproduces the main statistical properties observed in real traces. As concerns the offline/online interplay we provide a complete overview of the close connection between online and offline sociality. To reach our goal we gather data about offline contacts and social interactions on Facebook of a group of students and we propose a multidimensional network analysis which allows us to deeply understand how the characteristics of users in the distinct networks impact each other. Results show how offline and Facebook friends are different. This way we confirm and worsen the general intuition that online social networks have shifted away from their original goal to mirror the offline sociality of individuals. As for the role and the social importance, it becomes apparent that social features such as user popularity or community structure do not transfer along social dimensions, as confirmed by our correlation analysis of the network layers and by the comparison among the communities. In the last chapters we analyze the evolution of the online social network from a physical time perspective, i.e. considering the graph evolution as a graph time-series and not as a function of the network basic properties (number of nodes or links). As for the physical time in a user-centric viewpoint, we investigate the bursty nature of the link creation process in online social network. We prove not only that it is a highly inhomogeneous process, but also identify patterns of burstiness common to all nodes. Then we focus on the dynamic formation of two fundamental network building components: dyads and triads. We propose two new metrics to aid the temporal analysis on physical time: link creation delay and triangle closure delay. These two metrics enable us to study the dynamic creation of dyads and triads, and to highlight network behavior that would otherwise remain hidden. In our analysis, we find that link delays are generally very low in absolute time and are largely independent of the dates people join the network. To highlight the social nature of this metric, we introduce the term \textit{peerness} to quantify how well linked users overlap in lifetimes. As for triadic closure delay we first introduce an algorithm to extract of temporal triangle which enables us to monitor the triangle formation process, and to detect sudden changes in the triangle formation behavior, possibly related to external events. In particular, we show that the introduction of new service functionalities had a disruptive impact on the triangle creation process in the network

Follow the “mastodon”: Structure and evolution of a decentralized online social network

In this paper we present a dataset containing both the network of the \u201cfollow\u201d relationships and its growth in terms of new connections and users, all which we obtained by mining the decentralized online social network named Mastodon. The dataset is combined with usage statistics and meta-data (geographical location and allowed topics) about the servers comprising the platform-s architecture. These server are called instances. The paper also analyzes the overall structure of the Mastodon social network, focusing on its diversity w.r.t. other commercial microblogging platforms such as Twitter. Finally, we investigate how the instance-like paradigm influences the connections among the users. The newest and fastest-growing microblogging platform, Mastodon is set to become a valid alternative to established platforms like Twitter. The interest in Mastodon is mainly motivated as follows: a) the platform adopts an advertisement and recommendation-free business model; b) the decentralized architecture makes it possible to shift the control over user contents and data from the platform to the users; c) it adopts a community-like paradigm from both user and architecture viewpoints. In fact, Mastodon is composed of interconnected communities, placed on different servers; in addition, each single instance, with specific topics and languages, is independently owned and moderated. The released dataset paves the way to a number of research activities, which range from classic social network analysis to the modeling of social network dynamics and platform adoption in the early stage of the service. This data would also enable community detection validation since each instance hinges on specific topics and, lastly, the study of the interplay between the physical architecture of the platform and the social network it supports

Multidimensional human dynamics in mobile phone communications

In today's technology-assisted society, social interactions may be expressed through a variety of techno-communication channels, including online social networks, email and mobile phones (calls, text messages). Consequently, a clear grasp of human behavior through the diverse communication media is considered a key factor in understanding the formation of the today's information society. So far, all previous research on user communication behavior has focused on a sole communication activity. In this paper we move forward another step on this research path by performing a multidimensional study of human sociality as an expression of the use of mobile phones. The paper focuses on user temporal communication behavior in the interplay between the two complementary communication media, text messages and phone calls, that represent the bi-dimensional scenario of analysis. Our study provides a theoretical framework for analyzing multidimensional bursts as the most general burst category, that includes one-dimensional bursts as the simplest case, and offers empirical evidence of their nature by following the combined phone call/text message communication patterns of approximately one million people over three-month period. This quantitative approach enables the design of a generative model rooted in the three most significant features of the multidimensional burst - the number of dimensions, prevalence and interleaving degree - able to reproduce the main media usage attitude. The other findings of the paper include a novel multidimensional burst detection algorithm and an insight analysis of the human media selection process

Walls-in-one : usage and temporal patterns in a social media aggregator

The continual launches of new online social media that meet the most varied people\u2019s needs are resulting in a simultaneous adoption of different social platforms. As a consequence people are pushed to handle their identity across multiple platforms. However, due the to specialization of the services, people\u2019s identity and behavior are often partial, incomplete and scattered in different \u201cplaces\u201d. To overcome this identity fragmentation and to give an all-around picture of people\u2019s online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how and when users post their favorite contents, about their centrality on different social media and about the choice of their username. Specifically we gathered the posting activities and social sites usage from Alternion, a social media aggregator. The analysis of social media usage shows that Alternion data reflect the novel trend of today\u2019s users of branching out into different social platforms. However the novelty is the multidimensional and longitudinal nature of the dataset. Having at our disposal users\u2019 degree in five different social networks, we performed a rank correlation analysis on users\u2019 degree centrality and we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals\u2019 importance changes from medium to medium. The longitudinal nature of the dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Finally we show that users tend to use similar usernames to keep their identifiability across social sites

MULTIDIMENSIONAL ANALYSIS OF PEOPLE'S BEHAVIOR IN ONLINE SOCIAL NETWORKS

L\u2019impressionante crescita in popolarit\ue0 delle Online Social Networks (OSNs), evidenziata dall\u2019enorme numero di utenti oggi legati ai social network pi\uf9 popolari, offre un\u2019opportunit\ue0 unica per comprendere i comportamenti online degli individui. In questa tesi, analizziamo i comportamenti delle persone sulle OSNs considerando che tali comportamenti sono il risultato della combinazione di esperienze ed attitudini sia online che offline. Dapprima, eseguiamo una analisi multidimensionale degli utenti attraverso diversi social media per fornire una descrizione complessiva dei comportamenti online e comprendere come questi cambino quando pi\uf9 media sono disponibili contemporaneamente. I risultati che presentiamo rappresentano uno dei primi esempi di esplorazione dei comportamenti umani su diversi social media. Ad esempio, utilizzando lo user degree su 5 diversi social network, evidenziamo che l\u2019importanza di ogni individuo cambia da piattaforma a piattaforma. La natura longitudinale del nostro dataset \ue8 anche stata sfruttata per studiare l\u2019attivit\ue0 di posting degli utenti, evidenziando una leggera correlazione positiva sulla frequenza con cui gli utenti pubblicano su social media differenti e confermando la natura bursty delle attivit\ue0 di posting mediante l\u2019uso di serie temporali multidimensionali. Inoltre, durante la tesi abbiamo sviluppato un metodo di identificazione innovativo per collegare le persone attraverso le diverse piattaforme social. Facendo riferimento agli attributi pubblici comuni, attraverso l\u2019uso di application programming interface (API) dei diversi social network, costruiamo le istanze negative in tre modi diversi, superando la selezione randomica abitualmente adottata, allo scopo di valutare la robustezza del nostro algoritmo di identificazione su diversi dataset. I risultati mostrano che l\u2019approccio porta ad un metodo di identificazione molto efficace per costruire dataset affidabili. Uno scenario reale costruito su Google+ e Facebook \ue8 stato utilizzato come testbed per la validazione del metodo. I risultati che riportiamo dimostrano i vantaggi ottenibili con il nuovo metodo rispetto ad altri metodi da letteratura. Infine, la tesi compie un primo passo verso una miglior comprensione degli effetti degli eventi offline sulla struttura del grafo delle social network in cui sono pubblicizzati. Pi\uf9 precisamente, svolgiamo una analisi temporale della social network legata all\u2019evento, comprendendo le persone che dichiarano di partecipare all\u2019evento tramite facebook, e valutiamo come questa evolva durante l\u2019intervallo temporale dell\u2019eventi stesso. I risultati mostrano che nuove amicizie nascono durante l\u2019evento e che la creazione di questi nuovi legami sociali \ue8 una delle cause principali di chiusura triangolare e che il grado maggiore si osserva durante l\u2019ultimo giorno dell\u2019evento stesso.The unprecedented and quickly increasing popularity of Online Social Networks (OSNs) is evidenced by the huge number of users who are turning to Facebook, Twitter and other social networks. The rapid growth of these online social networks provides a unique chance to study and understand the online behavior of the people. In this thesis, we analyze people's behavior in online social network considering the fact that online behavior of people is influenced by different factors which derive from the combination of their offline and online life. First, we perform a multidimensional analysis of users across multiple social media sites to give an all-around picture of people\u2019s online behavior. While people in their online life have access to a wide portfolio of social platforms, little is known about users\u2019 behavior when they have different online communication media available. Our findings represent some novel insights about people\u2019s behavior across social media. Having at our disposal users\u2019 degree in five different social networks, we find that the individuals\u2019 importance changes from medium to medium. The longitudinal nature of our dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Second, we develop an innovative identification methodology for connecting people across multiple social platforms. Relying on common public attributes available through the official application programming interface (API) of social networks, we construct negative instances in three different ways, going beyond the commonly adopted random selection to evaluate the robustness of our identification algorithm on different datasets. Results show that the approach can lead to a very effective identification method and methodology for building reliable datasets. Moreover, we analyzed the success of our method in a real scenario built on Google+/Facebook neighborhoods. Experiments reveal the advantages of the proposed method in comparison to previous methods in the literature. Finally, we take the first step towards understanding the effect of offline events on the graph structure of the social network where they are advertised. More precisely, we perform a temporal analysis of the event social network, constituted by people declaring to attend the event on Facebook and the links between them, and evaluated how it evolves during the event time period. The results show that new friendships are created during events and that this new friendships creation is one of the main reasons of triangle closure and the higher degrees observed in the last day of the events period