With the advent of blockchain, Decentralised Finance (DeFi) has become an accessible and decentralised way to create financial services. Under the umbrella of DeFi services, we find Decentralised Exchanges (DEXs) i.e., smart contracts that allow one to exchange tokens. Over time, new malicious activities have begun to spread, particularly those related to DEXs. Maximal Extractable Value (MEV), a practice in which block creators can add, remove, or change the order of transactions to increase their gains at the expense of other users, is becoming pervasive.In this study, we collected a 2-year dataset and analysed the MEV activity on the Ethereum blockchain. With our analyses, we show that despite the countermeasures, the problem is still actual and that the value extracted could hinder the adoption of Ethereum in future projects. Furthermore, we provide an in-depth analysis of DEX platforms and tokens that are more susceptible to MEV attacks, showing that major markets are still far from solving the problem. Finally, we show that MEV attackers are becoming more sophisticated as they tend to chain different types of attacks (i.e., sandwich and arbitrage). Linked attacks are more profitable, as attackers extract more than 5 billion USD, while traditional attacks net 382 million USD.

The metaverse was recently proposed as a new way for people to socialise and interact with each other and digital objects in a world malleable at users' will. Blockchain plays a big role in the current metaverse proposals because it enables keeping track of digital assets' property via NFTs. Thanks to the blockchain, metaverses like Axie Infinity, also implement a rewarding system to keep players engaged in the platform following a Play 2 Earn model. However, users interested in the economic gain dynamics of such platforms may be encouraged to find alternative ways to earn by playing the game. In this paper, we discuss the Breed 2 Earn strategy used by players in the Axie Infinity metaverse to generate profit in the game. We identify two breeding methods to create new axies (in-game creatures used to access rewards) and study their efficacy using a 2-year dataset. Our analyses confirm that the two strategies are widely used, and by exploiting the bull run market they generate profits for breeders.

The InterPlanetary File System (IPFS) is among the most well-known distributed file systems applied in Web3 applications, where data storage is involved. Non-Fungible Tokens (NFTs) represent one of the main scenarios in which distributed content sharing is enacted via IPFS. Generally, NFTs are created, sold and bought via so-called marketplaces, easing the technical difficulties. They play a crucial role during the sale of NFTs because they act as intermediaries between users and underlying technologies. In this paper, we evaluate the impact of NFT marketplaces on the availability of NFT resources in the IPFS network. We focus on the relationship between resources and marketplaces, trying to understand whether access to some of the resources related to NFTs is transparent and consistent with the information present in the contract. We selected $\mathbf{1 0}$ collections and 3 NFTs for each collection and monitored these assets on IPFS. For the monitoring phase, we deployed our node and requested providers of the assets every six hours for two weeks. Our analysis shows that IPFS does not guarantee high availability and marketplaces refer to copies of NFTs, thus violating the integrity and security guaranteed by NFTs and IPFS.

With the advent of Web3, many online services have been revolutionised through decentralisation. With blockchain as the main decentralisation engine, online social media platforms have witnessed a new life. Recently, memes have come into play, providing new opportunities for community aggregation, giving birth to the so-called memecoin phenomenon. Memecoins are blockchain-backed cryptocurrencies that can be freely traded, and people can discuss them online. They represent a new scenario in which social and economic aspects are tightly intertwined, but external figures or events can also influence the activity of each token. In this paper, we provide an analysis of the relationship between the social and economic spheres of memecoins and how external factors influence their activity. We conducted our analyses on six case studies taken from different contexts and with unique histories, downloading data from various subreddits and blockchains. The findings show that each memecoin has unique distinctive features and that the activity around them is influenced by crypto-influencers, scams, or even external events.

When information dissemination campaigns on Online Social Networking platforms are too aggressive, this can easily cause information overexposure. Overexposure can break through the psychological and physiological limits that the audience can tolerate, making it difficult for the audience to obtain reasonable cognitive concepts. For example, the overexposure of information referring to an object in a promotional campaign can lead to inflated expectations in individuals. Building on this, we introduce two indicators for individuals’ expectations and actual utility of the object and design a multi-stage triggered mechanism for seed individuals to explore the relieving overexposure problem in information diffusion. We build a multi-stage information diffusion model and characterize the evolution of individual expectations. We verify the hardness result of the relieving overexposure problem by budget multi-stage allocation, and the non-submodularity and non-monotonicity of the objective function. Addressing the non-monotonic and non-submodular set function, we provide a direct influence-oriented algorithm with a greedy approach. Extensive experiments are performed on four real networks to explore how model parameters and network properties affect the effects of multi-stage triggered strategies for seed individuals. Using the experiments, we found that the seed individual multi-stage incremental triggered strategy of dissemination campaign of information referring to an object shows better performance, and the lower the actual utility of the specific object, the more accurate the promotion strategy needs to be developed.

This article provides an overview of the first part of the ACM TWEB’s Special Issue on Advances in Social Media Technologies and Analysis. It highlights both research and practical applications in this field.

This article provides an overview of the second part of the ACM TWEB’s Special Issue on Advances in Social Media Technologies and Analysis. It highlights both research and practical applications in this field.

Online social networks (OSNs) revolutionized how people interact with each other, and nowadays, thanks to blockchain technology, new solutions are being considered, giving birth to blockchain online social media (BOSMs). BOSMs use the blockchain to redistribute with their users the wealth generated by the platform through a rewarding system, assigning better rewards to socially impactful users. Thus, these new systems are characterized by highly intertwined economical and social aspects and constitute a new scenario in the world of social networks. Many scenarios, economic and social alike, show a phenomenon known as “the rich-get-richer,” which states that the richest actors of a system tend to become richer over time. To the best of our knowledge, in the scenario of BOSMs, where users can acquire cryptocurrency through their social actions, this type of phenomenon was not yet studied. In this article, we propose a methodological framework composed of three hypotheses that can help study the rich-get-richer phenomenon through a set of measures and indices. In addition, we apply the proposed framework to the Steem case study, showing how unevenly wealth is distributed on its blockchain and comparing our results to other scenarios.

The United Nations defined 17 Sustainable Development Goals (SDGs) to foster equitable, healthy, inclusive and safe communities. Clearly, they involve even social networks and, in particular, the sexuality expressed through them. For instance, consider sexting, the practice of sharing self-generated explicit content through mobile devices. Besides its popularity, this phenomenon carries several concerns, such as the possible damages caused by the spread of personal nude or semi-nude images without the owner’s consent. Unfortunately, messaging applications generally used to practice sexting are not safe enough as they permit to share any received content with anyone else. Aimed at preventing sexting-related adverse consequences for the wellness of people and creating safer, gender-equal and inclusive online communities, we discuss possible technological approaches to contrast the non-consensual spread of private self-generated content and, in particular, we analyse the impact of employing decentralised architectures in this context.

Abstract The rise of Non-Fungible Tokens (NFTs) is beginning to revolutionize the digital world thanks to the unique property of these tokens. Indeed, they can represent the ownership of physical or digital assets. They are implemented using smart contracts, therefore if the code of the smart contract contains bugs, an attacker can exploit its vulnerabilities to perform an attack called sleepminting. Sleepminting consists of transferring NFTs owned by an address, without the owner’s consent. In this paper, we provide a detailed analysis of the sleepminting attack and, thanks to the insights gained, we propose a prevention system to reduce the number of sleepminting attacks. Our prevention system is based on analysing the transactions included in new blocks, detecting those that are related to sleepminting attacks and keeping track of the addresses that are involved in these transactions. A dictionary-like data structure can be used to keep track of the addresses involved, where the key is the address and the value acts as a counter for the number of times the address is involved in sleepminting. With this information, block-creating nodes can add another verification step before adding a transaction to a block, which consists of blocking transactions when the addresses involved appear in sleepminting attacks a number of times greater than a threshold. The evaluation shows that sleepminting is a relevant phenomenon, and now it involves NFT transfers rather than NFT minting. Our proposed prevention system is able to block up to 87% of attacks.

Abstract Many decentralised systems can be represented as graphs, and the detection of their community structure can uncover important properties. Several community detection algorithms have been proposed, however, only a few solutions are suitable for detecting and managing communities in a distributed and highly dynamic environment. This lacking is mainly due to the difficulty of defining self-organising solutions in the presence of a high rate of dynamism. The main contribution of this paper is DISCO, a distributed protocol for community detection and management in a Peer-to-Peer dynamic environment. Our approach is mainly targeted to Decentralised Online Social Networks (DOSNs), but it can be applied in other distributed scenarios. In the context of DOSNs, DISCO allows to discover communities in the local social network of a user, named ego network, and to manage their evolution over time. DISCO is based on a Temporal Trade-off approach and exploits a set of super-peers for the management of the communities. The paper presents an extensive evaluation of the proposed approach based on a dataset gathered from Facebook and shows the ability of DISCO to orchestrate a set of nodes to detect and manage communities in a highly dynamic and decentralised environment. The paper also proposes a comparison with a state of the art approach, showing that it is capable of reducing the number of critical community lifecycle events by over 25%, and reducing the average loading factor by up to 50%. Graphical abstract

The Interplanetary File System (IPFS) seeks to build a decentralized, fast and efficient file system able to connect all devices worldwide. In particular, its decentralized nature makes it viable to be applied to other decentralized applications, such as Decentralized Online Social Networks. Several Blockchain Online Social Networks adopted IPFS for storing larger resources, such as videos, letting them claim to be censorship free platforms. In this paper we inspect whether IPFS is a good choice as data storage for Decentralised Social Applications, discussing its strengths and weaknesses related to our scenario. We face the problem of data storage and persistence thanks to the so-called “pinning” services implemented on top of IPFS. Additionally, we provide a set of analyses concerning physical location, protocols, and identity of the IPFS nodes discovered by our crawling node.

The impact of Online Social Networks (OSNs) on the world has changed the way people interact with each other. During the last years, the trend to build virtual communities based on common interests, also called online social groups, has affirmed. Online social groups have been studied in the past, however, several aspects concerning the relationships between the group members are still unknown. Indeed, the definition of classical friendship relations in a social network is different from the social relationships established in a group. The former are defined with the will of the users, while the latter are implicitly activated by the interactions on the common topics. In this paper, we provide an analysis of the users' relationships present in 18 heterogeneous Facebook groups and we model the interactions of a group member within the group with the concept of member network. Then, we verify that the relationships present in the personal network of a user follow the Dunbar's property, previously detected in offline and online friendship ego networks. The results show that most personal networks present three or four circles as in classical Dunbar's structure, with a size comparable to the one initially described by Dunbar.

Summary Online social networks (OSNs) have become one of the most popular platforms where people communicate by sharing contents and personal information. The interactions performed by the users allow to identify the homophily between users and reveal the presence of several communities that could depend on several factors: such as the type of relationships (eg, colleagues and school mates) or to users' preferences (eg, users' interests or hobbies). A very important issue in this scenario is the necessary to characterize such communities by using known real properties or attributes about their members. In this article, we propose an approach that identifies the communities of users by exploiting several community detection algorithms. Afterward, for each user, we exploit decision trees to find a model that describes and distinguishes community affiliations based on known attributes of the members. The evaluation of our approach is derived from a real dataset which consists of the profile information, relationships, and interactions of 95 716 Facebook users. The experimental results show that the proposed approach is able to correctly recognize which attributes of the members properly characterize their corresponding community while ensuring a high level of accuracy (about 85%).

Abstract In the last decades, temporal networks played a key role in modelling, understanding, and analysing the properties of dynamic systems where individuals and events vary in time. Of paramount importance is the representation and the analysis of Social Media, in particular Social Networks and Online Communities, through temporal networks, due to their intrinsic dynamism (social ties, online/offline status, users’ interactions, etc..). The identification of recurrent patterns in Online Communities, and in detail in Online Social Groups, is an important challenge which can reveal information concerning the structure of the social network, but also patterns of interactions, trending topics, and so on. Different works have already investigated the pattern detection in several scenarios by focusing mainly on identifying the occurrences of fixed and well known motifs (mostly, triads) or more flexible subgraphs. In this paper, we present the concept on the Incremental Communication Patterns, which is something in-between motifs, from which they inherit the meaningfulness of the identified structure, and subgraph, from which they inherit the possibility to be extended as needed. We formally define the Incremental Communication Patterns and exploit them to investigate the interaction patterns occurring in a real dataset consisting of 17 Online Social Groups taken from the list of Facebook groups. The results regarding our experimental analysis uncover interesting aspects of interactions patterns occurring in social groups and reveal that Incremental Communication Patterns are able to capture roles of the users within the groups.

Abstract Online Social Networking platforms (OSNs) have become part of people’s everyday life and their usage covers the deep-rooted need for communication among humans. During recent years, as people are questioning more and more OSN service providers, a new generation of proposals, based on blockchain became very popular thanks to the ethics adopted by these platforms. Steemit is the most important blockchain-based social networking site, which integrates, as main novelty an economic layer to the social media service. Steemit is implemented on top of Steem which, as in other blockchains, awards miners of the blocks with cryptocurrency. Steem miners, called witnesses, are not chosen based on the solution of a mathematical problem, as in Proof of Work based systems, but must be voted by other users. In this work, we decide to study the witnesses on Steem and their contribution to the social platform Steemit, and their social impact. We performed a set of analyses to shred light concerning their behaviour and to understand how they are socially perceived by other users. Analyses show an important social impact but, at the same time, some of them have a negative social impact. Their discussion is polarized towards content concerning Steem, Steemit, witnesses, and other platforms hosted on Steem.

Online social networks (OSNs) have changed the way of how people interact; however, lately, people are questioning more and more their business models. During the last ten years, new solutions based on decentralized architectures have been proposed, namely, decentralized OSNs (DOSNs) and blockchain online social medias (BOSMs). DOSNs were introduced several years ago and their main goal is the preservation of the privacy of the users in such a way that the data and the content of a user are always under their control. BOSMs leverage the usage of blockchain either to enforce the privacy of the users or to redistribute the wealth generated by the platform through a rewarding system. Steemit is the most stable and well-known BOSM with more than 1 million registered users, where users can create their own social network by following other users. To the best of our knowledge, no study exists on the relationship between the economic and social characteristics of BOSMs and on the way the rewarding system affects the social activity. The main goal of this article is to evaluate the characteristics of the Steemit follower-following graph to understand how the social and the economic aspects of BOSMs intertwine and influence each other. We study the properties of the Steemit follower-following graph and a few selected hotspot contents. The analysis shows that users are highly encouraged to be socially active, especially producing content, but the richest users are not also the most social ones, which suggests us that users can get rich without much involvement in the platform, using external mechanisms.

The widespread adoption of Online Social Networks (OSNs), the ever-increasing amount of information produced by their users, and the corresponding capacity to influence markets, politics, and society, have led both industrial and academic researchers to focus on how such systems could be influenced . While previous work has mainly focused on measuring current influential users, contents, or pages on the overall OSNs, the problem of predicting influencers in OSNs has remained relatively unexplored from a research perspective. Indeed, one of the main characteristics of OSNs is the ability of users to create different groups types, as well as to join groups defined by other users, in order to share information and opinions. In this article, we formulate the Influencers Prediction problem in the context of groups created in OSNs, and we define a general framework and an effective methodology to predict which users will be able to influence the behavior of the other ones in a future time period, based on historical interactions that occurred within the group. Our contribution, while rooted in solid rationale and established analytical tools, is also supported by an extensive experimental campaign. We investigate the accuracy of the predictions collecting data concerning the interactions among about 800,000 users from 18 Facebook groups belonging to different categories (i.e., News, Education, Sport, Entertainment, and Work). The achieved results show the quality and viability of our approach. For instance, we are able to predict, on average, for each group, around a third of what an ex-post analysis will show being the 10 most influential members of that group. While our contribution is interesting on its own and—to the best of our knowledge—unique, it is worth noticing that it also paves the way for further research in this field.

With the rapid development and growth of online social networks (OSNs), researchers have been pushed forward to improve the knowledge of these complex networks by analyzing several aspects, such as the types of social media, the structural properties of the network, or the interaction patterns among users. In particular, a relevant effort has been devoted to the study and identification of cohesive groups of users in OSNs (also referred as communities) because they are the basic building block of each OSN. While several research works on groups in OSNs have mainly focused on identifying the types of groups and the contents created by their members, the analysis of internal organizations of such groups remains unexplored due to the lack of real data sets containing information about such groups, about their members, and the interactions among them. In this article, we compensate for this shortcoming by studying the main properties of groups defined in OSNs, taking as reference use cases 40 real Facebook groups of different categories that account for a total of about 500.000 users. In particular, we exploit interaction patterns among users and social network analysis to uncover interesting aspects related to the internal organization of groups. Experimental results reveal that the majority of the collected groups exhibit an internal structure where members can be clustered in four subgroups according to the level of tie strength of the relations they have. Furthermore, clusters identified on Facebook groups can provide relevant information about the importance of users within such groups.

Summary The understanding of the user temporal behavior is a crucial aspect for all those systems that rely on user resources for daily operations, such as decentralized online social networks (DOSNs). Indeed, DOSNs exploit the devices of their users to take on and share the tasks needed to provide services such as storing the published data. In the last years, the increasing popularity of DOSN services has changed the way of how people interact with each other by enabling users to connect to these services at any time by using their personal devices (such as notebooks or smartphones). As a result, the availability of data in these systems is strongly affected (or reflected) by the temporal behavior of their users in terms of connections to DOSNs. In this paper, we propose the use of linear predictors to address the problem of the availability of user devices and, hence, data in DOSNs. To validate the proposed approaches, we evaluated their performance conducting a set of simulations exploiting a dataset of temporal information concerning the connections to Facebook collected from a set of users.

Centralized Online Social Networks (OSNs) have become the main communication channel in both the personal and the business domain. A current trend for developing OSN services is towards the distribution of the social network infrastructure by using P2P architectures as basis for Distributed Online Social Networks (DOSNs). One of the main challenges of DOSNs comes from guaranteeing privacy and protection of private data. In previous work [18], we proposed a Dunbar-based approach to preserve data availability in DOSNs. Using Dunbar's circles of intimacy a certain level of trust is ensured which bases on the users confidence in their friends. Now, to achieve privacy and anonymity, we focus on the incorporation of social contacts into existing Peer-to-Peer Overlays and show that a naive integration of social links into existing Overlays like Chord and Pastry is not satisfactory. In order to address drawbacks of the naive approach we introduce goLLuM, a general solution which can be used on top of existing structured and unstructured P2P networks. Our protocol enables to route messages via friendly nodes only, even if only few friends per node exist. By using synthetic models and real-data traces for the representation of friendship relationships we highlight the drawbacks of the naive solution and show the functionality of goLLuM.

P2P overlays based on Delaunay triangulations have been recently exploited to implement systems providing efficient routing and data broadcast solutions. Several applications such as Distributed Virtual Environments and geographical nearest neighbours selection benefit from this approach. This paper presents a novel distributed algorithm for the incremental construction of a Delaunay overlay in a P2P network. The algorithm employs a distributed version of the classical Edge Flipping procedure. Each peer builds the Delaunay links incrementally by exploiting a random peer sample returned by the underlying gossip level. The algorithm is then optimized by considering the Euclidean distance between peers to speed up the overlay convergence. We present theoretical results that prove the correctness of our approach along with a set of experiments that assess the convergence rate of the distributed algorithm.