Social media has gained importance as a channel to influence people's behavior and decisions, affecting not only the online world but also real-life (offline) events. This is especially evident in Brazil, where platforms like Telegram have been instrumental in disseminating political content rapidly and widely. However, the potential coordinated use of Telegram for promoting specific political narratives at critical times, such as the 2022 Brazilian elections, remains an area that requires further investigation. This study aims to investigate this phenomenon, focusing on the first and second rounds of voting and the January 8th riots. To this end, we conducted a comprehensive analysis of 620,000 messages from 256 Telegram groups, focusing on the dynamics of message dissemination and user interactions. Using network backbone extraction and text analysis methods, we identified key users who may be orchestrating the distribution of content. Our findings suggest that these individuals play a central role in the network's topology, relaying messages to a broader audience on dominant topics of discussion that reflect Brazil's political landscape during this turbulent period. This study not only highlights the growing influence of messaging apps on political mobilization but also contributes to our understanding of digital communication strategies in modern electoral contexts, emphasizing the need for further research in this field.

Big data applications and analytics are employed in many sectors for a variety of goals: improving customers satisfaction, predicting market behavior or improving processes in public health. These applications consist of complex software stacks that are often run on cloud systems. Predicting execution times is important for estimating the cost of cloud services and for effectively managing the underlying resources at runtime. Machine Learning (ML), providing black box solutions to model the relationship between application performance and system configuration without requiring in-detail knowledge of the system, has become a popular way of predicting the performance of big data applications. We investigate the cost-benefits of using supervised ML models for predicting the performance of applications on Spark, one of today's most widely used frameworks for big data analysis. We compare our approach with Ernest (an ML-based technique proposed in the literature by the Spark inventors) on a range of scenarios, application workloads, and cloud system configurations. Our experiments show that Ernest can accurately estimate the performance of very regular applications, but it fails when applications exhibit more irregular patterns and/or when extrapolating on bigger data set sizes. Results show that our models match or exceed Ernest's performance, sometimes enabling us to reduce the prediction error from 126-187% to only 5-19%.

Big data applications are among the most suitable applications to be executed on cluster resources because of their high requirements of computational power and data storage.Correctly sizing the resources devoted to their execution does not guarantee they will be executed as expected.Nevertheless, their execution can be affected by perturbations which can change the expected execution time.Identifying when these types of issue occurred by comparing their actual execution time with the expected one is mandatory to identify potentially critical situations and to take the appropriate steps to prevent them.To fulfill this objective, accurate estimates are necessary.In this paper, machine learning techniques coupled with a posteriori knowledge are exploited to build performance estimation models.Experimental results show how the models built with the proposed approach are able to outperform a reference state-of-the-art method (i.e., Ernest method), reducing in some scenarios the error from the 221.09-167.07%to 13.15-30.58%.

This paper studies the performance gains resulting from the sharing of energy and network resources in the case of co-located base stations of different mobile network operators, powered by photovoltaic panels, and equipped with energy storage. Three configurations are considered for base station cooperation. The first one assumes two non-cooperating base stations, each one exploiting its own power system and serving its own customers, hence with no sharing. The second considers a shared power system, but no cooperation in customer service. The third looks at cooperation in both energy production and service provisioning, since only one base station handles all customers when traffic is low. Using an analytical modeling framework, we compute performance metrics for the three cases, and we show that significant gains are possible in the case of energy and network sharing.

Companies that offer services on the Web often rely on randomized experiments known as A/B tests for assessing the impact of development and business decisions. During an experiment, each user is randomly redirected to one of two versions of the website, called treatments. Several response models were proposed to describe the behavior of a user in a social network website as a function of the treatment assigned to her and to her neighbors. However, there is no consensus as to which model should be applied to a given dataset. In this work, we propose a new response model, derive theoretical limits for the estimation error of several models, and obtain empirical results for cases where the response model was misspecified.

Online social networks are an important tool for people to share information and have been extensively used for people to achieve beneficial changes in health. Obesity is a major public health concern that affects about one third of the world's population. In order to alleviate this problem, health professionals are focusing on health interventions, which can be performed online. In this study we analyze three distinct online communities about weight and diet in Reddit. We model our data as 3 directed and weighted graphs of the posts and comments and evaluate the interaction between users of each community. We also analyze specific characteristics of each community, the habits of daily activity of the users and the formation of implicit bonds of friendship through the formation of communities. Our main results show that Reddit is a content-centered social network, in which what matters is what is posted and not who posts. In addition, users tend to create implicit friendship relationships through denser regions of interactions. Our results show that, contrary to expectations, the three communities present the same behavior pattern in a general point of view, which facilitates the development of non-directed online weight loss intervention strategies.

The alarming increase in the number of people afflicted by mental health disorders has become one of the major public health problems faced by governments worldwide. Traditional face-to-face clinical interventions are costly, and, in many cases, leave out a sizable number of people who are struggling to improve their mental health conditions. Alternative forms of intervention that have a larger reach and allow for continual interaction while reducing costs are being investigated, including those relying on Online Social Networks (OSNs). Initially designed for promoting friendship, OSNs started to connect people willing to share experiences related to mental health disorders. In light of this fact, we investigate four Reddit online communities: Depression, SuicideWatch, Anxiety and Bipolar. We focus on user activities and interactions, and on the discourse pattern analysis of posts and comments made by the community members. We found that (i) interaction patterns are very similar across these subreddits, and interactions are centered around content, rather than users; (ii) most posts that generate the longest discussion trees are requests for help and, more often than not, multiple users offer support; (iii) the four subreddits share a common language and encouragement words are a frequent pattern, for instance. We hope that the insights unveiled by our analyses will help on building successful online interventions to support people in crisis and assist their counselors.

Data heterogeneity and irregularity are key characteristics of big data applications that often overwhelm the existing software and hardware infrastructures. In such context, the exibility and elasticity provided by the cloud computing paradigm over a natural approach to cost-effectively adapting the allocated resources to the application's current needs. Yet, the same characteristics impose extra challenges to predicting the performance of cloud-based big data applications, a central step in proper management and planning. This paper explores two modeling approaches for performance prediction of cloud-based big data applications. We evaluate a queuing-based analytical model and a novel fast ad-hoc simulator in various scenarios based on different applications and infrastructure setups. Our results show that our approaches can predict average application execution times with 26% relative error in the very worst case and about 12% on average. Moreover, our simulator provides performance estimates 70 times faster than state of the art simulation tools.

In modern web, users contact lots of services, identified by the domain name of the server. The temporal sequence and transitions of visited domains form a trajectory of the user on the web. In this work, we analyze 4 weeks of such trajectories, extracted from logs collected in our university network, and mine them via big data and machine learning methodologies to extract the interests of users. Our goal is to create a model of such trajectories and find similarities so to observe peculiarity of users' browsing. Thanks to the model, we propose a methodology to automatically group together the trajectories of single users and/or communities into highly descriptive environments which in turn allow the analyst to identify the topic of interest. We propose an automatic way to highlight differences in terms of popularity and content of environments. Lastly, we analyze the transition among environments, showing how people in smaller communities, e.g., in the same department, have a much more homogeneous behaviour than people at large, e.g., in the university.

Personal Cloud Storage (PCS) is a very popular Internet service. It allows users to backup data to the cloud as well as to perform collaborative work while sharing content. Notably, content sharing is a key feature for PCS users. It however comes with extra costs for service providers, as shared files must be synchronized to multiple user devices, generating more downloads from cloud servers. Despite the increasing interest in this type of service, a thorough investigation on the costs and benefits of PCS for service providers and end users has not been conducted yet. To that end, we propose a model to analyze cost-benefit tradeoffs for both parties. We develop utility functions that capture, in an abstract level, the satisfaction of the service provider and users in various scenarios. Then, we apply our model to evaluate alternative policies for content sharing in PCS. We consider two alternative policies for the current PCS sharing architecture, which count on user collaboration to reduce providers' costs. Our results show that such policies are advantageous for providers and users, leading to 39% utility improvements for both parties, while requiring low commitment of resources from participating users.

We perform social analyses over an important community: the open code collaboration network. Specifically, we study the correlation among features that measure the strength of social coding collaboration on GitHub - a Web-based source code repository that can be modeled as a social coding network. We also make publicly available a curated dataset called GitSED, GitHub Socially Enhanced Dataset. Our results have many practical applications such as to improve the recommendation of developers, the evaluation of team formation and existing analysis algorithms.

In this work, we explore the increasing demand for novel user interfaces to navigate large media collections. We implement a geometric data structure to store and retrieve item-to-item similarity information and propose a novel navigation framework that uses vector operations and real-time user feedback to direct the outcome. The framework is scalable to large media collections and is suitable for computationally constrained devices. In particular, we implement this framework in the domain of music. To evaluate the effectiveness of the navigation process, we propose an automatic evaluation framework, based on synthetic user profiles, which allows us to quickly simulate and compare navigation paths using different algorithms and datasets. Moreover, we perform a real user study. To do that, we developed and launched Mixtape , a simple web application that allows users to create playlists by providing real-time feedback through liking and skipping patterns.

MOTIVATION: The 1000 Genomes Project (1KGP) and thousands of Genome-Wide Association Studies (GWAS) performed during the last years have generated an enormous amount of information that needs to be integrated to better understand the genetic architecture of complex diseases in different populations. This integration is important in areas such as genetics, epidemiology, anthropology, as well as admixture mapping design and GWAS-replications. Network-based approaches that explore the genetic bases of human diseases and traits have not yet incorporated information on genetic diversity among human populations. RESULTS: We propose Disease-ANCEstry networks (DANCE), a graph-based web tool that allows to integrate and visualize information on human complex phenotypes and their GWAS-hits, as well as their risk allele frequencies in different populations. DANCE provides an interactive way to explore the human SNP-Disease Network and its projection, a Disease-Disease Network. With these functionalities, DANCE fills a gap in our ability to handle and understand the knowledge generated by GWAS and 1KGP. We provide a number of case studies that show how DANCE can be used to explore the relationships between human complex diseases, their genetic bases and variability in different human populations. AVAILABILITY AND IMPLEMENTATION: DANCE is freely available at http://ldgh.com.br/dance/ We recommend using DANCE with Mozilla Firefox, Safari, Chrome or Internet Explorer (v9 or v10). CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cloud storage systems are currently very popular, generating a large amount of traffic. Indeed, many companies offer this kind of service, including worldwide providers such as Dropbox, Microsoft and Google. These companies, as well as new providers entering the market, could greatly benefit from knowing typical workload patterns that their services have to face in order to develop more cost-effective solutions. However, despite recent analyses of typical usage patterns and possible performance bottlenecks, no previous work investigated the underlying client processes that generate workload to the system. In this context, this paper proposes a hierarchical two-layer model for representing the Dropbox client behavior. We characterize the statistical parameters of the model using passive measurements gathered in 3 different network vantage points. Our contributions can be applied to support the design of realistic synthetic workloads, thus helping in the development and evaluation of new, well-performing personal cloud storage services.

P2P live streaming are increasingly popular nowadays. Due to their popularity, these systems may be a target of attacks and opportunistic user behavior. In this paper, we address the pollution attacks in such systems. We present a pollution damage model and also analyze a reputation system as a tool to fight attacks in P2P live streaming systems. The model we propose evidences that attacks are harmful even in a system with a small number of polluters. In this case, peers must have more than 3 times network bandwidth than they should have in a system without polluters. Our experimental results on PlanetLab show that just check data integrity is not an effective protection. In this case, we observe a very high data loss rate. Finally, the reputation system is effective against pollution attack. When peers do not whitewash their identities, the reputation system quickly identifies polluters. In this case, the overhead and loss rate can be negligible. During a whitewashing, the new approach presents less than 20% overhead and 3% of loss.

P2P-TV applications have attracted a lot of attention from the research community in the last years. Such systems generate a large amount of data which impacts the network performance. As a natural consequence, characterizing these systems has become a very important task to develop better multimedia systems. However, crawling data from P2P live streaming systems is particularly challenging by the fact that most of these applications have private protocols. In this work, we present a set of logs from a very popular P2P live streaming application, the SopCast. We describe our crawling methodology, and present a brief SopCast characterization. We believe that our logs and the characterization can be used as a starting point to the development of new live streaming systems.

Peer-to-Peer live video streaming systems are becoming increasingly popular. Nevertheless, in spite of various studies of client behavior aspects and system optimizations, the current knowledge about the dynamic properties of the system, particularly how the P2P overlay network changes over time during a live transmission, is still superficial. In this paper, we provide a characterization of the dynamic properties of a popular P2P live streaming media application, namely Sop Cast. We use complex network metrics to analyze how the structure of the network evolves over time from the perspective of individual nodes (local view) and of the whole network (global view). We find that Sop Cast peers may be clustered into three profiles based on their centrality properties in the network. Moreover, in spite of peers changing their partners over time, they tend to remain with the same centrality profile. Also, the global network structure tends to remain roughly stable over time, except for a decaying clustering coefficient. Our findings can be used to generate more realistic synthetic P2P workloads and to drive future system designs and simulations.

In this paper, we investigate the impact of peer bandwidth heterogeneity on the performance of a mesh-based P2P system for live streaming. We show that bandwidth heterogeneity constitutes an important resource for P2P live streaming systems. Indeed, by effectively exploiting it, the overall performance of the system is significantly improved. This requires the adoption of smart schemes for both the overlay topology construction and chunk scheduling mechanisms that discriminate among peers based on their bandwidth.

We consider large-scale mesh-based P2P systems for the distribution of real-time video content. Our goal is to study the impact that different design choices adopted while building the overlay topology may have on the system performance. In particular, we show that the adoption of different strategies leads to overlay topologies with different macroscopic properties. Representing the possible overlay topologies with different families of random graphs, we develop simple, yet accurate, fluid models that capture the dominant dynamics of the chunk distribution process over several families of random graphs. Our fluid models allow us to compare the performance of different strategies providing a guidance for the design of new and more efficient systems. In particular, we show that system performance can be significantly improved when possibly available information about peers location and/or peer access bandwidth is carefully exploited in the overlay topology formation process.

The increasing success of P2P-TV applications, that may overwhelm the network with their large volume of traffic in the near future, calls for the need of new traffic models that can effectively represent the traffic generated by these applications. In this paper, we study the traffic generated by PPLive and SopCast, that are among the most popular P2P-TV applications of today, and propose Hidden-Markov chains for modeling the traffic they generate. Our results show that the models are quite accurate and can be effectively used in many networking tasks such as network performance analysis, network planning and dimensioning, traffic engineering.

In this paper, we propose a simple and fully distributed mechanism for constructing and maintaining the overlay topology in mesh-based P2P-TV systems. Our algorithm optimizes the topology to better exploit large bandwidth peers, so that they are automatically moved close to the source. This improves the chunk delivery delay so that all peers benefit, not just the high bandwidth ones. A key property of the proposed scheme is its ability to indirectly estimate the upload bandwidth of peers without explicitly knowing or measuring it. Simulation results show that our scheme significantly outperforms overlays with homogeneous properties, achieving up to 50% performance improvement. Moreover, the algorithm is robust to both parameter setting and changing conditions, e.g., peer churning.

We consider the design of a P2P network for the distribution of real-time video streams through the Internet. We follow a multi-source approach where the stream is decomposed into several flows sent by different peers to each client. The goal is to resist to the frequent moves of the peers entering and leaving the network. We analyze our approach using the recently proposed PSQA technology which allows to obtain an accurate (and automatic) numerical evaluation of the quality as perceived by each client. Our transmission technique includes the use of an arbitrary amount of redundancy in the signal, whose specification is a part of the dimensioning process, and it works with very low signaling overhead. We illustrate with real data how the overall system allows to compensate efficiently the possible losses of frames due to peers leaving the network.

P2P-TV systems distribute live streaming contents by organizing the information flow in small chunks that are exchanged among peers. Different strategies can be implemented at the peers to select the chunk to distribute and the destination neighboring peer. Recent work showed that a good strategy consists in selecting the latest received chunk and a random neighboring peer (latest useful chunk, random peer). In this paper, leveraging on the idea that it is convenient to favor those peers that can contribute the most to the chunk distribution, we propose to select the destination peer with a probability proportional to the peer upload bandwidth. We show that the proposed scheme has a limited sensitivity to cheating peers that maliciously declare higher bandwidth than they actually have. Considering the overlay topology, we evaluate both systems in which nodes have fixed degree and systems whose overlay setup takes into account the actual peer bandwidth by assigning more neighbors to peer with higher bandwidth. We evaluate the performance in terms of delay percentiles and loss probability and evaluate the achieved improvements. Simulation results considering scenarios with up to 10,000 peers shows that the proposed schemes significantly outperform the traditional ones, so that the chunk distribution delay drops to less than 2 s from about 12 s.