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.

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.