Large Language Models (LLMs) raise concerns about lowering the cost of generating texts that could be used for unethical or illegal purposes, especially on social media. This paper investigates the promise of such models to help enforce legal requirements related to the disclosure of sponsored content online. We investigate the use of LLMs for generating synthetic Instagram captions with two objectives: The first objective (fidelity) is to produce realistic synthetic datasets. For this, we implement content-level and network-level metrics to assess whether synthetic captions are realistic. The second objective (utility) is to create synthetic data useful for sponsored content detection. For this, we evaluate the effectiveness of the generated synthetic data for training classifiers to identify undisclosed advertisements on Instagram. Our investigations show that the objectives of fidelity and utility may conflict and that prompt engineering is a useful but insufficient strategy. Additionally, we find that while individual synthetic posts may appear realistic, collectively they lack diversity, topic connectivity, and realistic user interaction patterns.

Modeling social media activity has numerous practical implications such as designing and testing intervention techniques to mitigate disinformation or delivering critical information during disaster relief operations. In this paper we propose a modeling technique that forecasts topic-specific daily volume of social media activities by using both exogenous signals, such as news or armed conflicts records, and endogenous data from the social media platform we model. Empirical evaluations with real datasets from Twitter on two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.

We investigate via simulation the conditions under which a new social convention promoted by a persistent minority can spread in a network polarized into two communities. Our experiments show how the fractional size of the persistent minority and its network location affect the network's tipping point. Specifically, we discover that i) previously research on the size of the critical mass need for widespread adoption does not apply in the presence of polarized communities; ii) spread is more affected by the position of the persistent minority than its size; and iii) topological properties of the nodes in the persistent minority, such as leverage centrality and degree, are relevant for the diffusion of the new social convention.

This paper introduces a decomposition approach to address the problem of predicting different user activities at hour granularity over a long period of time. Our approach involves two steps. First, we used a temporal neural network ensemble to predict the number of each type of activity that occurred in a day. Second, we used a set of neural networks to assign the events to a user-repository pair in a particular hour. We focused this work on a subset of the public GitHub dataset that records the activities of over 2 million users on over 400,000 software repositories. Our experiments show we were able to predict hourly user-repo activity with reasonably low error. Our simulations are accurate for 1-3 weeks (168-504 hours) after inception, with accuracy gradually falling off. It was shown that activity on Twitter and Reddit increases the accuracy of activity prediction on GitHub for most events.

Activity on social media is seen as a relevant sensor for different aspects of the society. In a heavily digitized society, security vulnerabilities pose a significant threat that is publicly discussed on social media. This study presents a comparison of user-generated content related to security vulnerabilities on three digital platforms: two social media conversation channels (Reddit and Twitter) and a collaborative software development platform (GitHub). Our data analysis shows that while more security vulnerabilities are discussed on Twitter, relevant conversations go viral earlier on Reddit. We show that the two social media platforms can be used to accurately predict activity on GitHub.

The tradeoff between anonymity and utility in the context of the anonymization of graph data sets is well acknowledged; for better privacy, some of the graph structural properties must be lost. What is not well understood, however, is what forces shape this tradeoff. Specifically, for the data practitioner who wants to publish an anonymized graph data set, it is unclear what graph structural properties can be preserved and what are the anonymity costs associated with preserving them. This article proposes a framework that examines the interplay between graph properties and the vulnerability to deanonymization attacks. We demonstrate its applicability via extensive experiments on thousands of graphs with controlled properties generated from real data sets. In addition, we show empirically that there are structural properties that affect graph vulnerability to reidentification attacks independent of degree distribution.

Online games provide a rich recording of interactions that can contribute to understanding human behavior. One potential contribution is understanding what motivates people to choose their teammates. We examine several hypotheses about team formation using a large, longitudinal dataset from a team-based online gaming environment. Specifically, we test how positive familiarity, homophily, and competence determine team formation in Battlefield 4, a popular team-based game in which players choose one of two competing teams to play on. Our dataset covers over two months of in-game interactions between over 380,000 players. We show that familiarity is an important factor in team formation, while homophily is not. Competence affects team formation in more nuanced ways: players with similarly high competence team up repeatedly, but large variation in competence discourages repeated interactions.

Community-based question answering (CQA) platforms are crowd-sourced services for sharing user expertise on various topics, from mechanical repairs to parenting. While they naturally build-in an online social network infrastructure, they carry a very different purpose from Facebook-like social networks, where users "hang-out" with their friends and tend to share more personal information. It is unclear, thus, how the privacy concerns and their correlation with user behavior in an online social network translate into a CQA platform. This study analyzes one year of recorded traces from a mature CQA platform to understand the association between users' privacy concerns as manifested by their account settings and their activity in the platform. The results show that privacy preference is correlated with behavior in the community in terms of engagement, retention, accomplishments and deviance from the norm. We find privacy-concerned users have higher qualitative and quantitative contributions, show higher retention, report more abuses, have higher perception on answer quality and have larger social circles. However, at the same time, these users also exhibit more deviant behavior than the users with public profiles.

Community-based question answering platforms can be rich sources of information on a variety of specialized topics, from finance to cooking. The usefulness of such platforms depends heavily on user contributions (questions and answers), but also on respecting the community rules. As a crowd-sourced service, such platforms rely on their users for monitoring and flagging content that violates community rules. Common wisdom is to eliminate the users who receive many flags. Our analysis of a year of traces from a mature Q&A site shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83%.

An unprecedented information wealth produced by online social networks, further augmented by location/collocation data, is currently fragmented across different proprietary services. Combined, it can accurately represent the social world and enable novel socially aware applications. We present Prometheus, a socially aware peer-to-peer service that collects social information from multiple sources into a multigraph managed in a decentralized fashion on user-contributed nodes, and exposes it through an interface implementing nontrivial social inferences while complying with user-defined access policies. Simulations and experiments on PlanetLab with emulated application workloads show the system exhibits good end-to-end response time, low communication overhead, and resilience to malicious attacks.

Friend-to-Friend (F2F) storage systems were shown to suffer from two significant limitations. First, users with a small set of friends are penalized by lack of available storage for their needs, while users with many friends get overloaded with resource requests. Second, friends are typically in close geographical proximity to each other, and thus their online times are synchronized, leading to low data availability when they are offline. This paper addresses these concerns by expanding the set of storage resources while still using a measure of social incentives. It proposes an indirect tie measurement to compute the social strength between possibly distant nodes in a social network. Using datasets from co-authorship networks and a video gaming community, we show that the social strength-based mechanism more than doubles the set of storage candidates motivated by social incentives, invites socially low connected users to contribute more resources and improves data availability by up to 6.5 times. We also show that our method complemented with simple load balancing strategies significantly improves peer engagement and workload distribution in F2F storage systems.

Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and spoils the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit it, to victims’ monetary losses due to cyber crime. This article studies cheaters in the Steam Community, an online social network built on top of the world’s dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also observed timing information of the cheater flags, as well as the dynamics of the cheaters’ social neighborhoods. We discovered that cheaters are well embedded in the social and interaction networks: their network position is largely indistinguishable from that of fair players. Moreover, we noticed that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community. Also, we observed a social penalty involved with being labeled as a cheater: cheaters lose friends immediately after the cheating label is publicly applied. Most importantly, we observed that cheating behavior spreads through a social mechanism: the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. This allows us to propose ideas for limiting cheating contagion.

Social applications mine user social graphs to improve performance in search, provide recommendations, allow resource sharing and increase data privacy. When such applications are implemented on a peer-to-peer (P2P) architecture, the social graph is distributed on the P2P system: the traversal of the social graph translates into a socially-informed routing in the peer-to-peer layer. In this work we introduce the model of a projection graph that is the result of decentralizing a social graph onto a peer-to-peer network. We focus on three social network metrics: degree, node betweenness and edge betweenness centrality and analytically formulate the relation between metrics in the social graph and in the projection graph. We demonstrate with an application scenario on real social networks the usability of the projection graph properties in designing social search applications and unstructured P2P overlays that exhibit improved performance and reduced overhead over baseline scenarios.

Voice and data traffic growth over the last several years has become a major challenge for cellular operators with a direct impact on revenues, infrastructure investments, and end-user performance. The economics of these operators depend on various incentives used to attract users in the form of unlimited, buffet-like voice/sms/data packages. However, our understanding of the effects of user behavior under these offerings on operator revenues/costs remains poor. Using two years of detailed usage information of ~1 million users across three services, voice, sms and data, combined with payment and cost information, we study how user behavior affects the economics of cellular operators. We discover that around 20% of the users consume more resources than what they pay for and hence are non-profitable. In addition to the individual user behavior, we study how the user interactions in the call graph affect the operator's revenues and cost, drawing on tools from social network analysis. We develop a framework that incorporates both the individual and social user behavior for studying how volume caps influence the revenues and the traffic costs. Using this framework we empirically show that volume caps can increase the difference between the revenues and the traffic costs of the studied operator by a factor of 2, while affecting only 16% of the existing user base.

P2P13 was held in Trento, Italy September 9–11 2013. Peer-to-Peer Computing is a small, single track, focussed conference on Peer-to-Peer technologies that is today recognized as one of the most relevant venues for the publication and dissemination of results on Peer-to-Peer technologies, applications, systems, and networks.

Combining and incorporating rich semantics of user social data, which is currently fragmented and managed by proprietary applications, has the potential to more accurately represent a user's social ecosystems. However, social ecosystems raise even more serious privacy concerns than today's social networks. This paper proposes to model privacy as contextual integrity by using semantic web tools and focuses on defining default privacy policies, as they have the highest impact. Through a real implementation and performance evaluation we show that such a framework is practical.

Online gaming is a multi-billion dollar industry that entertains a large, global population. One unfortunate phenomenon, however, poisons the competition and the fun: cheating. The costs of cheating span from industry-supported expenditures to detect and limit cheating, to victims' monetary losses due to cyber crime. This paper studies cheaters in the Steam Community, an online social network built on top of the world's dominant digital game delivery platform. We collected information about more than 12 million gamers connected in a global social network, of which more than 700 thousand have their profiles flagged as cheaters. We also collected in-game interaction data of over 10 thousand players from a popular multiplayer gaming server. We show that cheaters are well embedded in the social and interaction networks: their network position is largely indistinguishable from that of fair players. We observe that the cheating behavior appears to spread through a social mechanism: the presence and the number of cheater friends of a fair player is correlated with the likelihood of her becoming a cheater in the future. Also, we observe that there is a social penalty involved with being labeled as a cheater: cheaters are likely to switch to more restrictive privacy settings once they are tagged and they lose more friends than fair players. Finally, we observe that the number of cheaters is not correlated with the geographical, real-world population density, or with the local popularity of the Steam Community.

Social applications implemented on a peer-to-peer (P2P) architecture mine the social graph of their users for improved performance in search, recommendations, resource sharing and others. In such applications, the social graph that connects their users is distributed on the peer-to-peer system: the traversal of the social graph translates to a socially-informed routing in the peer-to-peer layer. In this work we introduce the model of a projection graph that is the result of mapping a social graph onto a peer-to-peer network. We analytically formulate the relation between metrics in the social graph and in the projection graph. We focus on three such graph metrics: degree centrality, node betweenness centrality, and edge betweenness centrality. We evaluate experimentally the feasibility of estimating these metrics in the projection graph from the metrics of the social graph. Our experiments on real networks show that when mapping communities of 50-150 users on a peer, there is an optimal organization of the projection graph with respect to degree and node betweenness centrality. In this range, the association between the properties of the social graph and the projection graph is the highest, and thus the properties of the (dynamic) projection graph can be inferred from the properties of the (slower changing) social graph. We discuss the applicability of our findings to aspects of peer-to-peer systems such as data dissemination, social search, peer vulnerability, and data placement and caching.

Web caches, content distribution networks, peer-to-peer file-sharing networks, distributed file systems, and data grids all have in common that they involve a community of users who use shared data. In each case, overall system performance can be improved significantly by first identifying and then exploiting the structure of community's data access patterns. We propose a novel perspective for analyzing data access workloads that considers the implicit relationships that form among users based on the data they access. We propose a new structure-the interest-sharing graph-that captures common user interests in data and justify its utility with studies on four data-sharing systems: a high-energy physics collaboration, the Web, the Kazaa peer-to-peer network, and a BitTorrent file-sharing community. We find small-world patterns in the interest-sharing graphs of all four communities. We investigate analytically and experimentally some of the potential causes that lead to this pattern and conclude that user preferences play a major role. The significance of small-world patterns is twofold: it provides a rigorous support to intuition and it suggests the potential to exploit these naturally emerging patterns. As a proof of concept, we design and evaluate an information dissemination system that exploits the small-world interest-sharing graphs by building an interest-aware network overlay. We show that this approach leads to improved information dissemination performance.

Abstract Group communication primitives have broad utility as building blocks for distributed applications. The challenge is to create and maintain the distributed structures that support these primitives while accounting for volatile end‐nodes and variable network characteristics. Most solutions proposed to date rely on complex algorithms or on global information, thus limiting the scale of deployments and acceptance outside the academic realm. This article introduces a low‐complexity, self‐organizing solution for building and maintaining data dissemination trees, which we refer to as Unstructured Multi‐source Overlay (UMO). UMO uses traditional distributed systems techniques: layering, soft‐state, and passive data collection to adapt to the dynamics of the physical network and maintain data dissemination trees. The result is a simple, adaptive system with lower overheads than more complex alternatives. We implemented UMO and evaluated it on a 100‐node PlanetLab testbed and on up to 1024‐node emulated ModelNet networks. Extensive experimental evaluations demonstrate UMOs low overhead, efficient network usage compared with alternative solutions, and the ability to quickly adapt to network changes and to recover from failures. Copyright © 2009 John Wiley & Sons, Ltd.

Distributed computing systems employ replication to improve overall system robustness, scalability, and performance. A replica location service (RLS) offers a mechanism to maintain and provide information about physical locations of replicas. This paper defines a design framework for RLSs that supports a variety of deployment options. We describe the RLS implementation that is distributed with the Globus toolkit and is in production use in several grid deployments. Features of our modular implementation include the use of soft-state protocols to populate a distributed index and Bloom filter compression to reduce overheads for distribution of index information. Our performance evaluation demonstrates that the RLS implementation scales well for individual servers with millions of entries and up to 100 clients. We describe the characteristics of existing RLS deployments and discuss how RLS has been integrated with higher-level data management services.

The analysis of data usage in a large set of real traces from a high-energy physics collaboration revealed the existence of an emergent grouping of files that we coined filecules. This paper presents the benefits of using this file grouping for prestaging data and compares it with previously proposed file grouping techniques along a range of performance metrics. Our experiments with real workloads demonstrate that filecule grouping is a reliable and useful abstraction for data management in science Grids; that preserving time locality for data prestaging is highly recommended; that job reordering with respect to data availability has significant impact on throughput; and finally, that a relatively short history of traces is a good predictor for filecule grouping.Our experimental results provide lessons for workload modeling and suggest design guidelines for data management in data-intensive resource-sharing environments.

Grid computing has reached the stage where deployments are mature and many collaborations run in production mode. Mature grid deployments offer the opportunity for revisiting and perhaps updating traditional beliefs related to workload models, which in turn leads to the re-evaluation of traditional resource management techniques. This paper analyzes usage patterns in a typical grid community, a large-scale data-intensive scientific collaboration in high-energy physics. We focus mainly on data usage, since data is the major resource for this class of applications. Our observations led us to propose a new abstraction for resource management in scientific data analysis applications: we define a filecule as a group of files that is always used together. We show that filecules exist and present their characteristics. The existence of filecules suggests a new granularity for data management, which, if incorporated in design, can significantly outperform the traditional solutions for data caching, replication and placement based on single-file granularity. We reason about the impact of filecules on resource management and show compelling evidence for using this abstraction when designing data management services

Grid computing environments need secure resource control and predictable service quality in order to be sustainable. We propose a grid hosting model in which independent, self-contained grid deployments run within isolated containers on shared resource provider sites. Sites and hosted grids interact via an underlying resource control plane to manage a dynamic binding of computational resources to containers. We present a prototype grid hosting system, in which a set of independent Globus grids share a network of cluster sites. Each grid instance runs a coordinator that leases and configures cluster resources for its grid on demand. Experiments demonstrate adaptive provisioning of cluster resources and contrast job-level and container-level resource management in the context of two grid application managers.

Information dissemination is a fundamental and frequently occurring problem in large, dynamic, distributed systems. We propose a novel approach to this problem, interest-aware information dissemination, that takes advantage of small-world usage patterns in data-sharing communities. These small-world characteristics suggest that users naturally form groups of common interest. We propose algorithms for identifying these groups dynamically, without a need for explicit classification of topics or declaration of user interests. These algorithms use information about the data consumed by users to identify, via online computation, groups with similar interests. As a proof of concept, we apply this methodology to the problem of locating files in large user communities. Using real-world traces from a scientific community and from a peer-to-peer system, we show that proactive information dissemination within groups of common interest can reduce the search load by up to 70%. In addition, this approach naturally supports the efficient discovery of collections of files, a requirement specific to scientific data analysis tasks. We hypothesize that our algorithms can find numerous other uses in distributed systems, such as reputation management.

Web caches, content distribution networks, peer-to-peer file sharing networks, distributed file systems, and data grids all have in common that they involve a community of users who generate requests for shared data. In each case, overall system performance can be improved significantly if we can first identify and then exploit interesting structure within a community's access patterns. To this end, we propose a novel perspective on file sharing that considers the relationships that form among users based on the files in which they are interested. We propose a new structure that captures common user interests in data - the data-sharing graph - and justify its utility with studies on three data-distribution systems: a high-energy physics collaboration, the Web, and the Kazaa peer-to-peer network. We find small-world patterns in the data-sharing graphs of all three communities. We analyze these graphs and propose some probable causes for these emergent small-world patterns. The significance of small-world patterns is twofold: it provides a rigorous support to intuition and, perhaps most importantly, it suggests ways to design mechanisms that exploit these naturally emerging patterns.

Efficient data sharing in global peer-to-peer systems is complicated by erratic node failure, unreliable network connectivity and limited bandwidth. Replicating data on multiple nodes can improve availability and response time. Yet determining when and where to replicate data in order to meet performance goals in large-scale systems with many users and files, dynamic network characteristics, and changing user behavior is difficult. We propose an approach in which peers create replicas automatically in a decentralized fashion, as required to meet availability goals. The aim of our framework is to maintain a threshold level of availability at all times. We identify a set of factors that hinder data availability and propose a model that decides when more replication is necessary. We evaluate the accuracy and performance of the proposed model using simulations. Our preliminary results show that the model is effective in predicting the required number of replicas in the system.

Computational grids provide mechanisms for sharing and accessing large and heterogeneous collections of remote resources such as computers, online instruments, storage space, data, and applications. Resources are requested by specifying a set of desired attributes. Resource attributes have various degrees of dynamism, from mostly static attributes, such as operating system version, to highly dynamic ones, such as available network bandwidth or CPU load. Another dimension of dynamism is introduced by variable and highly diverse sharing policies: resources are made available to the grid community based on locally defined and potentially changing policies.

In wide area computing systems, it is often desirable to create remote read-only copies (replicas) of files. Replication can be used to reduce access latency, improve data locality, and/or increase robustness, scalability and performance for distributed applications. We define a replica location service (RLS) as a system that maintains and provides access to information about the physical locations of copies. An RLS typically functions as one component of a data grid architecture. This paper makes the following contributions. First, we characterize RLS requirements. Next, we describe a parameterized architectural framework, which we name Giggle (for GIGa-scale Global Location Engine), within which a wide range of RLSs can be defined. We define several concrete instantiations of this framework with different performance characteristics. Finally, we present initial performance results for an RLS prototype, demonstrating that RLS systems can be constructed that meet performance goals.

The idle computers on a local area, campus area, or even wide area network represent a significant computational resource-one that is, however, also unreliable, heterogeneous, and opportunistic. We describe an algorithm that allows branch-and-bound problems to be solved in such environments. In designing this algorithm, we faced two challenges: (1) scalability, to effectively exploit the variably sized pools of resources available, and (2) fault tolerance, to ensure the reliability of services. We achieve scalability through a fully decentralized algorithm, in which the dynamically available resources are managed through a membership protocol. We guarantee fault tolerance in the sense that the loss of up to all but one resource will not affect the quality of the solution. For propagating information reliably, we use epidemic communication for both the membership protocol and the fault-tolerance mechanism. We have developed a simulation framework that allows us to evaluate design alternatives. Results obtained in this framework suggest that our techniques can execute scalably and reliably.