Discovering and managing dynamic communities in DOSNs

Community structure is one of the most studied features of Online Social Networks (OSNs). Community detection guarantees several advantages for both centralized and decentralized social networks. Decentralized Online Social Networks (DOSNs) have been proposed to provide more control over private data. One of the main challenge in DOSNs concerns the availability of social data and communities can be exploited to guarantee a more efficient solution about the data availability problem. The detection of communities and the management of their evolution represents a hard process, especially in highly dynamic social networks, such as DOSNs, where the online/offline status of user changes very frequently. In this paper, we focus our attention on a preliminary analysis of dynamic community detection in DOSNs by studying a real Facebook dataset to evaluate how frequent the communities change over time and which events are more frequent. The results prove that the social graph has a high instability and distributed solutions to manage the dynamism are needed

Towards the next generation social network

Online Social Networks (OSNs) is part of everyday life for many people, but many question them because they fail to preserve the users' privacy. Therefore scientists tried to propose distributed architectures for the implementation of OSNs, giving birth to Distributed Online Social Networks (DOSN). To fully embrace the decentralization, the knowledge of how people use OSNs is needed, and in this thesis, we propose analyses to cover the lack of knowledge and contributions towards a next-generation DOSN. We start by analyzing how community detection can be beneficial to OSNs in a static and dynamic fashion, and design a privacy policy recommendation system. We then propose Incremental Communication Patterns to capture malicious users, such as bots or stalkers. We also turn our attention to the scenario Online Social Groups, in which we study the interaction structures of its users. To support the decentralization, we propose an innovative social overlay, called Contextual Ego Network based on contexts, a distributed dynamic community detection and management protocol, and a study of the InterPlanetary File System, and discuss their application in DOSNs. Lastly, we focused on Blockchain Online Social Media by taking Steemit as a case study. We started by studying the interaction graph and the follower-following graph of the users. Additionally, we analyzed the features of the users, gaining insights concerning the topics discussed and the behavior of block producers and bots

Interaction Communities in Blockchain Online Social Media

Surfing Online Social Media (OSM) websites have become a daily activity for a large number of people worldwide. People use OSMs to satisfy their innate need to socialise, but also as a source of information or to share personal facts. Thanks to the massive success of cryptocurrencies, the blockchain technology gained popularity among researchers, giving birth to a new generation of social media. Steemit is the most well-known blockchain-based social media, and it is based on the public blockchain Steem. Steemit employs Steem as data storage, and to implement a rewarding mechanism that grants cryptocurrency to pieces of content that are considered relevant by the users. Steem represents the first experiment that integrates OSMs and an economic rewarding system on the same platform, and in this paper, we inspect the interactions among the users from a community perspective. We apply two community detection algorithms on five graphs that model just as many facets of the Steem blockchain and test the detected structure against three measures for community structure evaluation. Findings show that communities tend to be very large, index of how much users are encouraged to interact as much as possible, and in particular, in the monetary graph, we detect a large number of the block producers of Steem

From NFT 1.0 to NFT 2.0: A Review of the Evolution of Non-Fungible Tokens

Non-fungible tokens (NFT) represent one of the most important technologies in the space of Web3. Thanks to NFTs, digital or physical assets can be tokenised to represent their ownership through the usage of smart contracts and blockchains. The first generation of this technology, called NFT 1.0, considers static tokens described by a set of metadata that cannot be changed after token creation. The static nature prevents their wide spread as they do not support any meaningful user interaction. For this reason, its evolution, called NFT 2.0, has been proposed to make tokens interactive and dynamic and enhance user experience, opening the possibility to use NFTs in more ways and scenarios. The purpose of this article is to review the transition from NFT 1.0 to NFT 2.0, focusing on the newly introduced properties and features and the rising challenges. In particular, we discuss the technical aspects of blockchain technology and its impact on NFTs. We provide a detailed description of NFT properties and standards on various blockchains and discuss the support of the most important blockchains for NFTs. Then, we discuss the properties and features introduced by NFT 2.0 and detail the technical challenges related to metadata and dynamism. Lastly, we conclude by highlighting the new application scenarios opened by NFT 2.0. This review paper serves as a solid base for future research on the topic as it highlights the current technological challenges that must be addressed to help a wide adoption of NFTs 2.0

Dynamic Community Structure in Online Social Groups

One of the main ideas about the Internet is to rethink its services in a user-centric fashion. This fact translates to having human-scale services with devices that will become smarter and make decisions in place of their respective owners. Online Social Networks and, in particular, Online Social Groups, such as Facebook Groups, will be at the epicentre of this revolution because of their great relevance in the current society. Despite the vast number of studies on human behaviour in Online Social Media, the characteristics of Online Social Groups are still unknown. In this paper, we propose a dynamic community detection driven study of the structure of users inside Facebook Groups. The communities are extracted considering the interactions among the members of a group and it aims at searching dense communication groups of users, and the evolution of the communication groups over time, in order to discover social properties of Online Social Groups. The analysis is carried out considering the activity of 17 Facebook Groups, using 8 community detection algorithms and considering 2 possible interaction lifespans. Results show that interaction communities in OSGs are very fragmented but community detection tools are capable of uncovering relevant structures. The study of the community quality gives important insights about the community structure and increasing the interaction lifespan does not necessarily result in more clusterized or bigger communities

Social games and Blockchain: exploring the Metaverse of Decentraland

Online Social Networks gained a crucial role in people's everyday life, acting as the medium through which people can interact with each other. The introduction of blockchain technology prompted a new generation of social media based on the concept of Non-Fungible Token (NFT) and other Web3 technologies, giving birth to the Metaverse. Decentraland is one such platform, where users can explore a 3D virtual world and communicate with each other. The main feature of Decentraland is that the virtual world is divided into parcels, implemented through NFTs, that can be traded among users and where owners can create buildings or mini-games for other users to play with. In this work, we explore the virtual world of Decentraland by presenting the main details of the virtual world and by focusing on the economic impact of NFT trading on the description of the parcels. In detail, the parcel's description can be used to advertise the proximity of the parcel to infrastructures or special projects to attract potential buyers. This paper's findings show the impact and the magnitude of this phenomenon, highlighting entire quarters that adopt this technique throughout the map by overriding the playful aspect of the platform

Studying micro-communities in facebook communities

In the visionary view of the future Internet, named the Next Generation Internet, a current idea is to have a user-centric approach where human behavior models will be used to define the networks or to manage services. During the last years, a great trend in current Social Media platforms is to offer the opportunity to establish and join groups of people online. Despite human behaviour in current Online Social Media have been studied in depth, characteristics of these aggregations of people in content-based communities are still unknown. In this paper, we propose an evaluation of micro-communities of users inside the big network of Facebook groups to understand how and when users are active, and to evaluate the evolution of these micro-communities over time. Results show that almost all groups showed interactions-based communities. We found out that in all cases there is one massive core community which attracts small communities

A Graph-Based Socioeconomic Analysis of Steemit

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