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Disengagement from social media platforms encompasses various behaviours, such as reducing activity or abandoning them altogether. At the far end of this spectrum, permanently deleting user accounts holds substantial implications for individuals, the people they contact, and the platform itself. However, the motivations underlying this choice, which involves erasing all content and social connections rather than simply abandoning the platform, remain unclear. To address this research gap, we analysed 4.5 million active users on Tumblr, a microblogging service. We assessed the likelihood of users deleting their accounts using binary logistic regression in general and in specific clusters of users. Our findings reveal that users are more inclined to delete their accounts if they have previously removed content from their profiles, highlighting the role of account deletion in cleaning and controlling personal information. Additionally, we observed robust peer correlations, indicating that users are more likely to delete their accounts if those they follow have already taken this step. We discuss these findings in the context of non-use theories and propose the development of checking-out processes that can enhance the user experience associated with account deletion. [ABSTRACT FROM AUTHOR]

In a large network of computers or wireless sensors, each of the components (henceforth, peers) has some data about the global state of the system. Much of the system's functionality such as message routing, information retrieval, and load sharing relies on modeling the global state. We refer to the outcome of the function (e.g., the load experienced by each peer) as the model of the system. Since the state of the system is constantly changing, it is necessary to keep the models up to date. Computing global data mining models, e.g., decision trees, k-means clustering in large distributed systems may be very costly due to the scale of the system and due to communication cost, which may be high. The cost further increases in a dynamic scenario when the data changes rapidly. In this paper, we describe a two-step approach for dealing with these costs. First, we describe a highly efficient local algorithm that can be used to monitor a wide class of data mining models. Then, we use this algorithm as a feedback loop for the monitoring of complex functions of the data such as its k-means clustering. The theoretical claims are corroborated with a thorough experimental analysis. Index Terms--Distributed data mining, peer-to-peer, local algorithms.

Classification based on decision trees is one of the important problems in data mining and has applications in many fields. In recent years, database systems have become highly distributed, and distributed system paradigms, such as federated and peer-to-peer databases, are being adopted. In this paper, we consider the problem of inducing decission trees in a large distributed network of genomic databases. Our work is motivated by the existence of distributed databases in healthcare and in bioinformatics, and by the emergence of systems which automatically analyze these databases, and by the expectancy that these databases will soon contain large amounts of highly dimensional genomic data. Current decision tree algorithms require high communication bandwidth when executed on such data, which are large-scale distributed systems. We present an algorithm that sharply reduces the communication overhead by sending just a fraction of the statistical data. A fraction which is nevertheless sufficient to derive the exact same decision tree learned by a sequential learner on all the data in the network. Extensive experiments using standard synthetic SNP data show that the algorithm utilizes the high dependency among attributes, typical to genomic data, to reduce communication overhead by up to 99 percent. Scalability tests show that the algorithm scales well with both the size of the data set, the dimensionality of the data, and the size of the distributed system. Index Terms--Data mining, distributed algorithms, decision trees, classification.

We extend the problem of association rule mining--a key data mining problem--to systems in which the database is partitioned among a very large number of computers that are dispersed over a wide area. Such computing systems include grid computing platforms, federated database systems, and peer-to-peer computing environments. The scale of these systems poses several difficulties, such as the impracticality of global communications and global synchronization, dynamic topology changes of the network, on-the-fly data updates, the need to share resources with other applications, and the frequent failure and recovery of resources. We present an algorithm by which every node in the system can reach the exact solution, as if it were given the combined database. The algorithm is entirely asynchronous, imposes very little communication overhead, transparently tolerates network topology changes and node failures, and quickly adjusts to changes in the data as they occur. Simulation of up to 10000 nodes show that the algorithm is local: all rules, except for those whose confidence is about equal to the confidence threshold, are discovered using information gathered from a very small vicinity, whose size is independent of the size of the system. Index Terms--Anytime algorithms, association rule-mining, data mining, local algorithms, peer-to-peer.

Local thresholding algorithms were first presented more than a decade ago and have since been applied to a variety of data mining tasks in peer-to-peer systems, wireless sensor networks, and in grid systems. One critical assumption made by those algorithms has always been cycle-free routing. The existence of even one cycle may lead all peers to the wrong outcome. Outside the lab, unfortunately, cycle freedom is not easy to achieve. This work is the first to lift the requirement of cycle freedom by presenting a local thresholding algorithm suitable for general network graphs. The algorithm relies on a new repositioning of the problem in weighted vector arithmetics, on a new stopping rule, whose proof does not require that the network be cycle free, and on new methods for balance correction when the stopping rule fails. The new stopping and update rules permit calculation of the very same functions that were calculable using previous algorithms, which do assume cycle freedom. The algorithm is implemented on a standard peer-to-peer simulator and is validated for networks of up to 80,000 peers, organized in three different topologies representative of major current distributed systems: the Internet, structured peer-to-peer systems, and wireless sensor networks. [ABSTRACT FROM AUTHOR]

Whereas in the past the issue of privacy has been examined from legal and philosophical perspectives, the present study attempts to examine the social mechanisms that define the boundaries of privacy by means of Social Representations Theory. Social representations simultaneously refer to the actions of both individual and group, and consequently belonging to several social groups means the coexistence of discrete and at times conflicting social representations. As long as the representations are not confronted with their dissonance and inconsistency - oftentimes due to privacy - they can coexist. The issue emerges in conflict situations wherein the individual has to choose a mode of action from several incompatible possibilities. The argument on which the present article is based is that privacy serves as a social mechanism whose purpose is to enable individuals to shift between inconsistent identities. We shall present three test cases to illustrate how society, by means of its agents, constantly sets and destroys boundaries of privacy. These cases illuminate a complex system that functions in a dialectic of interrelations that apply pressure in two directions: the individual on society, and society on the individual. The innovation proposed in the present article is in viewing privacy as a mechanism for maintaining inconsistency between identities. Central to this is the shared interest of both group and individual to enable the coexistence of the individual's different identities, whose social representations are incompatible. [ABSTRACT FROM AUTHOR]