Suchergebnisse

Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network "friends" is developed to demonstrate the applicability of artificial neural networks for virtual community research.

Online social networks have increasing influence on our society, they may play decisive roles in politics and can be crucial for the fate of companies. Such services compete with each other and some may even break down rapidly. Using social network datasets we show the main factors leading to such a dramatic collapse. At early stage mostly the loosely bound users disappear, later collective effects play the main role leading to cascading failures. We present a theory based on a generalised threshold model to explain the findings and show how the collapse time can be estimated in advance using the dynamics of the churning users. Our results shed light to possible mechanisms of instabilities in other competing social processes.

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.

New technologies in communications and networking have shaped the way political movements can be mobilised and coordinated in important ways. Recent uprisings have shown dramatically how a people can communicate its cause effectively beyond borders, through online social networking channels and mobile phone technologies. Hannah Arendt, as an eminent scholar of power and politics in the modern era, offers a relevant lens with which to theoretically examine the implications and uses of online social networks and their impact on politics as praxis. This article creates an account of how Arendt might have evaluated virtual social networks in the context of their potency to create power, spaces and possibilities for political action. With an Arendtian lens the article examines whether these virtual means of 'shared appearances' facilitate or frustrate efforts in the formation of political power and the creation of new beginnings. Based on a contemporary reading of her writings, the article concludes that Arendt's own assessment of online social networks, as spheres for political action, would likely have been very critical. ; Peer-reviewed ; Post-print

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

International audience ; Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithm lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements , an analytical model and a fairness-based News Feed design. The model leverages the versatility and analytical tractability of time-to-live (TTL) counters to capture the visibility and occupancy of publishers over a News Feed. Measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility and occupancy bias incurred by users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition , we find that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views, motivating the proposal of a novel and more transparent fairness-based News Feed design.

Intro -- Acknowledgments -- Introduction -- Chapter Overview -- Terminology and Definitions -- Attributes -- Privacy Settings of Attributes -- Risk Sources -- Data Inference -- Threat -- Privacy Harm -- Privacy Risks -- Privacy Risk Analysis -- Dimensions of Privacy Scoring in OSNs -- Type of Data -- Assumptions About the User -- Privacy Settings -- Risk Sources -- Privacy Metrics -- Sensitivity -- Visibility -- Reachability -- Data Inference -- Suggestion of Countermeasures -- Attribute Visibility in OSN -- Visibility Matrix -- Construction of Visibility Matrix -- An Illustration -- Open Problems -- Harm Trees for OSNs -- Harm Trees -- Construction of Harm trees -- Harm Likelihood -- Harm Expressions -- Harm Database -- Open Problems -- Privacy Risk Analysis in OSNs -- An Overview -- PrivOSN in Details -- Computation of Accuracy -- Evaluation of Harm Likelihoods -- Computation Profile Similarity -- Presentation of Privacy Risk to the User -- Residual Risks -- Open Problems -- Social Benefits -- Social Ties -- Social Capital -- Understanding Social Benefits -- Social Benefit Criteria -- Attributes and Social Benefit Criteria -- Evaluation of Social Benefit -- Open Problems -- Choosing the Right Privacy Settings -- Privacy and Social Benefit Tradeoff in Privacy Management -- An Integer Programming Model -- Balancing Privacy Risks and Social Benefits -- Formulation of the IP Problem -- Objective Function -- Privacy Risk Constraints -- Social Benefit Constraints -- Privacy Setting Constraints -- Open Problems -- Conclusion -- Notations and Their Meanings -- Comparison of Privacy Scoring Mechanisms -- Cases 3 and 4 -- Bibliography -- Authors' Biographies -- Blank Page.

New technologies in communications and networking have shaped the way political movements can be mobilised and coordinated in important ways. Recent uprisings have shown dramatically how a people can communicate its cause effectively beyond borders, through online social networking channels and mobile phone technologies. Hannah Arendt, as an eminent scholar of power and politics in the modern era, offers a relevant lens with which to theoretically examine the implications and uses of online social networks and their impact on politics as praxis. This article creates an account of how Arendt might have evaluated virtual social networks in the context of their potency to create power, spaces and possibilities for political action. With an Arendtian lens the article examines whether these virtual means of 'shared appearances' facilitate or frustrate efforts in the formation of political power and the creation of new beginnings. Based on a contemporary reading of her writings, the article concludes that Arendt's own assessment of online social networks, as spheres for political action, would likely have been very critical.

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.