Text watermarking e Social Network: uno studio sperimentale

La diffusione dei Social Network ha portato alla necessità di utilizzare tecniche per fare copyright e autenticazione dei file su di essi diffusi. Viene presentato un metodo di watermarking testuale basato sulla sostituzione dei caratteri omoglifi e studiato nell'ambiente dei Social Network. E' stata posta particolare attenzione sulla possibilità che questi adottino già tecniche di watermarking testuale e successivamente sono state studiate le potenzialità dell'algoritmo proposto sulle diverse piattaforme, valutandone la percentuale di successo, la robustezza e l'invisibilità.

Social network e watermarking: alcuni risultati preliminari

La diffusione dei social network ha permesso ad un elevato numero di persone di condividere i propri contenuti multimediali (testo, foto, video) con una larga platea di contatti. Potenzialmente questi contenuti possono essere condivisi anche con persone non direttamente collegate al proprietario. Uno dei comportamenti più diffuso degli utenti dei social network è la condivisione di foto. In questo contesto diventa importante riconoscere e preservare la proprietà di un'immagine. Lo studio effettuato in questo documento quindi, si prefigge lo scopo di controllare se i social network inseriscano un qualche watermark all'interno dell'immagine caricata. L'elaborato inoltre cerca di capire, analizzando e testando vari algoritmi di watermarking su immagini condivise, come le firme digitali vengano inserite all'interno di una foto e come queste rispondano alle alterazioni da parte dei social network.

How do Facebook users interact and what are the underlying reasons that trigger these interactions

This research was conducted to understand how Facebook users interact and the underlying reasons for doing so with a focus on one-to-mass communication interactions. Different methods and sources were used to generate accurate and valid insights. It was discovered that liking, groups, commenting, events and sharing are essential interactions, whereby liking, commenting and sharing were investigated in more detail. This investigations proves that emotions do trigger these three interactions; The most influencing emotions are Surprise/Wonder, Deep Respect/ Impressiveness and Fun/Joy. Moreover a variety of specific factors that trigger each of the interactions are revealed.

Investigation of the Network Construction Behaviour on Social Networking Sites

By enabling connections between individuals, Social Networking Sites, such as Facebook, promise to create significant individual as well as social value. Encouraging connections between users is also crucial for service providers who increasingly rely on social advertising and viral marketing campaigns as important sources of their revenue. Consequently, understanding user’s network construction behavior becomes critical. However, previous studies offer only few scattered insights into this research question. In order to fill this gap, we employ Grounded Theory methodology to derive a comprehensive model of network construction behavior on social networking sites. In the following step we assess two Structural Equation Models to gain refined insights into the motivation to send and accept friendship requests – two network expansion strategies. Based on our findings, we offer recommendations for social network providers.

Do social capital building strategies influence the financing behavior of Chinese private small and medium-sized enterprises?

Using data from 65,485 Chinese private small and medium-sized enterprises over the period 2000-2006, we examine the extent to which firms can improve access to debt by adopting strategies aimed at building social capital, namely entertaining and gift giving to others in their social network, and obtaining political affiliation. We find that although entertainment and gift-giving expenditure leads to higher levels of total and short-term debt, it does not enable firms to obtain greater long-term debt. In contrast, we demonstrate that obtaining political affiliation allows firms greater access to long-term debt.

A social matching system for an online dating network : a preliminary study

Due to the change in attitudes and lifestyles, people expect to find new partners and friends via various ways now-a-days. Online dating networks create a network for people to meet each other and allow making contact with different objectives of developing a personal, romantic or sexual relationship. Due to the higher expectation of users, online matching companies are trying to adopt recommender systems. However, the existing recommendation techniques such as content-based, collaborative filtering or hybrid techniques focus on users explicit contact behaviors but ignore the implicit relationship among users in the network. This paper proposes a social matching system that uses past relations and user similarities in finding potential matches. The proposed system is evaluated on the dataset collected from an online dating network. Empirical analysis shows that the recommendation success rate has increased to 31% as compared to the baseline success rate of 19%.

A semi-supervised graph-based algorithm for detecting outliers in online-social-networks

In this paper, we propose a semi-supervised approach of anomaly detection in Online Social Networks. The social network is modeled as a graph and its features are extracted to detect anomaly. A clustering algorithm is then used to group users based on these features and fuzzy logic is applied to assign degree of anomalous behavior to the users of these clusters. Empirical analysis shows effectiveness of this method.

A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) lambda-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The lambda-coverage problem is concerned with finding a set of k key nodes having minimal size that can influence a given percentage lambda of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the lambda-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient. Note to Practitioners-In recent times, social networks have received a high level of attention due to their proven ability in improving the performance of web search, recommendations in collaborative filtering systems, spreading a technology in the market using viral marketing techniques, etc. It is well known that the interpersonal relationships (or ties or links) between individuals cause change or improvement in the social system because the decisions made by individuals are influenced heavily by the behavior of their neighbors. An interesting and key problem in social networks is to discover the most influential nodes in the social network which can influence other nodes in the social network in a strong and deep way. This problem is called the target set selection problem and has two variants: 1) the top-k nodes problem, where we are required to identify a set of k influential nodes that maximize the number of nodes being influenced in the network and 2) the lambda-coverage problem which involves finding a set of influential nodes having minimum size that can influence a given percentage lambda of the nodes in the entire network. There are many existing algorithms in the literature for solving these problems. In this paper, we propose a new algorithm which is based on a novel interpretation of information diffusion in a social network as a cooperative game. Using this analogy, we develop an algorithm based on the Shapley value of the underlying cooperative game. The proposed algorithm outperforms the existing algorithms in terms of generality or computational complexity or both. Our results are validated through extensive experimentation on both synthetically generated and real-world data sets.

Turnout Intention and Social Networks

How can networking affect the turnout in an election? We present a simple model to explain turnout as a result of a dynamic process of formation of the intention to vote within Erdös-Renyi random networks. Citizens have fixed preferences for one of two parties and are embedded in a given social network. They decide whether or not to vote on the basis of the attitude of their immediate contacts. They may simply follow the behavior of the majority (followers) or make an adaptive local calculus of voting (Downsian behavior). So they either have the intention of voting when the majority of their neighbors are willing to vote too, or they vote when they perceive in their social neighborhood that elections are "close". We study the long run average turnout, interpreted as the actual turnout observed in an election. Depending on the combination of values of the two key parameters, the average connectivity and the probability of behaving as a follower or in a Downsian fashion, the system exhibits monostability (zero turnout), bistability (zero turnout and either moderate or high turnout) or tristability (zero, moderate and high turnout). This means, in particular, that for a wide range of values of both parameters, we obtain realistic turnout rates, i.e. between 50% and 90%.

Prosocial norms and degree heterogeneity in social networks

We provide empirical evidence to support the claims that social diversity promotes prosocial behavior. We elicit a real-life social network and its members’ adherence to a social norm, namely inequity aversion. The data reveal a positive relationship between subjects’ prosociality and several measures of centrality. This result is in line with the theoretical literature that relates the evolution of social norms to the structure of social interactions and argues that central individuals are crucial for the emergence of prosocial behavior.

Are they adicctied to college students social networks?

The development of the Internet and in particular of social networks has supposedly given a new view to the different aspects that surround human behavior. It includes those associated with addictions, but specifically the ones that have to do with technologies. Following a correlational descriptive design we present the results of a study, which involved university students from Social and Legal Sciences as participants, about their addiction to the Internet and in particular to social networks. The sample was conformed of 373 participants from the cities of Granada, Sevilla, Málaga, and Córdoba. To gather the data a questionnaire that was design by Young was translated to Spanish. The main research objective was to determine if university students could be considered social network addicts. The most prominent result was that the participants don’t consider themselves to be addicted to the Internet or to social networks; in particular women reflected a major distance from the social networks. It’s important to know that the results differ from those found in the literature review, which opens the question, are the participants in a phase of denial towards the addiction?

Social networks applied to training

In this article, we address the importance and relevance that social networks exhibit in their use as an educational resource.  This relevance relies in the possibility of implementing new learning resources or increasing the level of the participant's connectivity, as well as developing learning communities.  Also, the risk entailed from their use is discussed, especially for the students that have a low technological education or those having excessive confidence on the media.  It is important to highlight that the educational use of social networks is not a simple extension or translation of the student's habitual, recreational use, but that it implies an important change in the roles given to teachers as well as learners; from accommodative learning environments that only encourage memorization to other environments that demand an active, reflective, collaborative and proactive attitude, that require the development/acquisition of technological as well as social abilities, aptitudes and values.  It is also important to highlight that a correct implementation and adequate use will not only foment formal learning, but also informal and non-formal learning.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

La délinquance chez les jeunes : une évaluation approfondie de l’enchâssement social et du chevauchement des sphères relationnelles

Il est bien établi dans la littérature en criminologie que les pairs délinquants peuvent avoir un effet néfaste sur les comportements d’un jeune. L’analyse des caractéristiques de l’entourage social est donc essentielle à une compréhension globale des conduites individuelles. Puisqu’il est impossible pour un jeune, délinquant ou non, de se distancer complètement du monde conventionnel (Sykes et Matza, 1957; Warr 2002), il importe de considérer le chevauchement des relations conventionnelles et délinquantes pour saisir l’ampleur du phénomène de l’influence social. De surcroît, le réseau social des jeunes ne se limite pas à leurs amis, les membres de la famille, les collègues de classe et de travail pouvant aussi avoir une influence sur les comportements. La présente étude propose une analyse de l’entourage social de 237 jeunes âgés de 14 à 24 ans, fréquentant les organismes communautaires au Québec. Les résultats révèlent que: 1) la participation à un délit chez les jeunes en communauté est fréquente, 2) les caractéristiques du réseau social, reflétant l’enchâssement social, ne se trouvent pas révélatrices de la participation à un délit, 3) côtoyer les membres de son réseau social en grande intensité réduit de manière significative le volume de délits de marché commis dans une année, et ce, même en contrôlant la présence de contacts délinquants dans le réseau, 4) la présence de contacts délinquants dans plus d’une sphère relationnelle composant le réseau social permet de créer un index de dispersion de la délinquance reflétant ainsi l’enchâssement criminel des jeunes et finalement 5) plus les contacts délinquants sont dispersés à travers les sphères relationnelles, plus le risque de participation à un délit augmente. Toutefois, la dispersion des contacts délinquants dans le réseau social ne prédit pas la fréquence des délits commis. À des fins d’intervention, connaître la dispersion de la délinquance dans le réseau social peut aider à la prévention des comportements délinquants.